{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANN Classification of 4 class SMR dataset from BCI Competition VI dataset 2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from ./BCICIV_2a_gdf/A01T.gdf...\n",
      "GDF file detected\n",
      "Overlapping events detected. Use find_edf_events for the original events.\n",
      "Setting channel info structure...\n",
      "Interpolating stim channel. Events may jitter.\n",
      "Creating raw.info structure...\n",
      "Channel names are not unique, found duplicates for: {'EEG'}. Applying running numbers for duplicates.\n",
      "<Info | 17 non-empty fields\n",
      "    bads : list | 0 items\n",
      "    buffer_size_sec : float | 1.0\n",
      "    ch_names : list | EEG-Fz, EEG-0, EEG-1, EEG-2, EEG-3, EEG-4, EEG-5, ...\n",
      "    chs : list | 26 items (EEG: 25, STIM: 1)\n",
      "    comps : list | 0 items\n",
      "    custom_ref_applied : bool | False\n",
      "    dev_head_t : Transform | 3 items\n",
      "    events : list | 0 items\n",
      "    highpass : float | 0.5 Hz\n",
      "    hpi_meas : list | 0 items\n",
      "    hpi_results : list | 0 items\n",
      "    lowpass : float | 100.0 Hz\n",
      "    meas_date : int | 1105963200\n",
      "    nchan : int | 26\n",
      "    proc_history : list | 0 items\n",
      "    projs : list | 0 items\n",
      "    sfreq : float | 250.0 Hz\n",
      "    acq_pars : NoneType\n",
      "    acq_stim : NoneType\n",
      "    ctf_head_t : NoneType\n",
      "    description : NoneType\n",
      "    dev_ctf_t : NoneType\n",
      "    dig : NoneType\n",
      "    experimenter : NoneType\n",
      "    file_id : NoneType\n",
      "    gantry_angle : NoneType\n",
      "    hpi_subsystem : NoneType\n",
      "    kit_system_id : NoneType\n",
      "    line_freq : NoneType\n",
      "    meas_id : NoneType\n",
      "    proj_id : NoneType\n",
      "    proj_name : NoneType\n",
      "    subject_info : NoneType\n",
      "    xplotter_layout : NoneType\n",
      ">\n",
      "['EEG-Fz', 'EEG-0', 'EEG-1', 'EEG-2', 'EEG-3', 'EEG-4', 'EEG-5', 'EEG-C3', 'EEG-6', 'EEG-Cz', 'EEG-7', 'EEG-C4', 'EEG-8', 'EEG-9', 'EEG-10', 'EEG-11', 'EEG-12', 'EEG-13', 'EEG-14', 'EEG-Pz', 'EEG-15', 'EEG-16', 'EOG-left', 'EOG-central', 'EOG-right', 'STI 014']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\mne-0.16.1-py3.6.egg\\mne\\io\\edf\\edf.py:1028: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
      "  etmode = np.fromstring(etmode, np.uint8).tolist()[0]\n",
      "<ipython-input-6-74dc835d69eb>:8: RuntimeWarning: Overlapping events detected. Use find_edf_events for the original events.\n",
      "  raw = mne.io.read_raw_edf(filename)\n",
      "<ipython-input-6-74dc835d69eb>:8: RuntimeWarning: Interpolating stim channel. Events may jitter.\n",
      "  raw = mne.io.read_raw_edf(filename)\n",
      "<ipython-input-6-74dc835d69eb>:8: RuntimeWarning: Channel names are not unique, found duplicates for: {'EEG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(filename)\n"
     ]
    }
   ],
   "source": [
    "import mne\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "# Mention the file path to the dataset\n",
    "filename = \"./BCICIV_2a_gdf/A01T.gdf\"\n",
    "\n",
    "raw = mne.io.read_raw_edf(filename)\n",
    "\n",
    "print(raw.info)\n",
    "print(raw.ch_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Events and Epoch Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 672527  =      0.000 ...  2690.108 secs...\n",
      "Setting up band-pass filter from 7 - 35 Hz\n",
      "l_trans_bandwidth chosen to be 2.0 Hz\n",
      "h_trans_bandwidth chosen to be 8.8 Hz\n",
      "Filter length of 413 samples (1.652 sec) selected\n",
      "288 matching events found\n",
      "No baseline correction applied\n",
      "Not setting metadata\n",
      "0 projection items activated\n",
      "Loading data for 288 events and 751 original time points ...\n",
      "0 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "# Find the events time positions\n",
    "edf_events = mne.io.find_edf_events(raw)\n",
    "\n",
    "# Change the event matrix to epoch readable format\n",
    "\n",
    "events = (np.vstack((edf_events[1],edf_events[3],edf_events[2]))).T\n",
    "\n",
    "# Pre-load the data\n",
    "\n",
    "raw.load_data()\n",
    "\n",
    "# Filter the raw signal with a band pass filter in 7-35 Hz\n",
    "\n",
    "raw.filter(7., 35., fir_design='firwin')\n",
    "\n",
    "# Remove the EOG channels and pick only desired EEG channels\n",
    "\n",
    "raw.info['bads'] += ['EOG-left', 'EOG-central', 'EOG-right']\n",
    "\n",
    "picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=False, stim=False,\n",
    "                       exclude='bads')\n",
    "\n",
    "# Extracts epochs of 3s time period from the datset into 288 events for all 4 classes\n",
    "\n",
    "tmin, tmax = 1., 4.\n",
    "event_id = dict(left_hand = 769,right_hand = 770,foot = 771,tongue = 772)\n",
    "\n",
    "epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,\n",
    "                baseline=None, preload=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Epoch Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Evoked  |  'left_hand' (mean, N=72), [1, 4] sec, 22 ch, ~174 kB>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Evoked  |  'right_hand' (mean, N=72), [1, 4] sec, 22 ch, ~174 kB>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Evoked  |  'foot' (mean, N=72), [1, 4] sec, 22 ch, ~174 kB>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Evoked  |  'tongue' (mean, N=72), [1, 4] sec, 22 ch, ~174 kB>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Left hand epoch average plot\n",
    "\n",
    "evoked = epochs['left_hand'].average()\n",
    "print(evoked)\n",
    "evoked.plot(time_unit='s')\n",
    "\n",
    "# Right hand epoch average plot\n",
    "\n",
    "evoked = epochs['right_hand'].average()\n",
    "print(evoked)\n",
    "evoked.plot(time_unit='s')\n",
    "\n",
    "# Foot epoch average plot\n",
    "\n",
    "evoked = epochs['foot'].average()\n",
    "print(evoked)\n",
    "evoked.plot(time_unit='s')\n",
    "\n",
    "# Tongue epoch average plot\n",
    "\n",
    "evoked = epochs['tongue'].average()\n",
    "print(evoked)\n",
    "evoked.plot(time_unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = epochs.events[:,-1] - 769 + 1\n",
    "\n",
    "data = epochs.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wavelet Packet Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pywt\n",
    "\n",
    "# signal is decomposed to level 5 with 'db4' wavelet\n",
    "\n",
    "def wpd(X): \n",
    "    coeffs = pywt.WaveletPacket(X,'db4',mode='symmetric',maxlevel=5)\n",
    "    return coeffs\n",
    "             \n",
    "def feature_bands(x):\n",
    "    \n",
    "    Bands = np.empty((8,x.shape[0],x.shape[1],30)) # 8 freq band coefficients are chosen from the range 4-32Hz\n",
    "    \n",
    "    for i in range(x.shape[0]):\n",
    "        for ii in range(x.shape[1]):\n",
    "             pos = []\n",
    "             C = wpd(x[i,ii,:]) \n",
    "             pos = np.append(pos,[node.path for node in C.get_level(5, 'natural')])\n",
    "             for b in range(1,9):\n",
    "                 Bands[b-1,i,ii,:] = C[pos[b]].data\n",
    "        \n",
    "    return Bands\n",
    "\n",
    "wpd_data = feature_bands(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mne.decoding import CSP # Common Spatial Pattern Filtering\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import regularizers\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "# OneHotEncoding Labels\n",
    "enc = OneHotEncoder()\n",
    "X_out = enc.fit_transform(labels.reshape(-1,1)).toarray()\n",
    "\n",
    "# Cross Validation Split\n",
    "cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "acc = []\n",
    "ka = []\n",
    "prec = []\n",
    "recall = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_classifier():\n",
    "    classifier = Sequential()\n",
    "    classifier.add(Dense(units = 124, kernel_initializer = 'uniform', activation = 'relu', input_dim = 32, \n",
    "                         kernel_regularizer=regularizers.l2(0.01))) # L2 regularization\n",
    "    classifier.add(Dropout(p = 0.5))\n",
    "    for itr in range(1):\n",
    "        classifier.add(Dense(units = 124, kernel_initializer = 'uniform', activation = 'relu', \n",
    "                             kernel_regularizer=regularizers.l2(0.01))) # L2 regularization\n",
    "        classifier.add(Dropout(p = 0.5))    \n",
    "    classifier.add(Dense(units = 4, kernel_initializer = 'uniform', activation = 'softmax'))\n",
    "    classifier.compile(optimizer = 'rmsprop' , loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "    return classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dropout` call to the Keras 2 API: `Dropout(rate=0.5)`\n",
      "  \"\"\"\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Dropout` call to the Keras 2 API: `Dropout(rate=0.5)`\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "230/230 [==============================] - 5s 22ms/step - loss: 1.4706 - acc: 0.4348\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 1.2393 - acc: 0.6130\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 1.0366 - acc: 0.5391\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.9477 - acc: 0.6043\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.9174 - acc: 0.6000\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.8693 - acc: 0.6261\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.8751 - acc: 0.6087\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.8342 - acc: 0.6913\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.7957 - acc: 0.7391\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.7362 - acc: 0.7565\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.7614 - acc: 0.7174\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.6923 - acc: 0.7913\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.6691 - acc: 0.8130\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.6239 - acc: 0.7957\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.6183 - acc: 0.8348\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.6136 - acc: 0.8261\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 444us/step - loss: 0.5818 - acc: 0.8391\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.5440 - acc: 0.8739\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.5946 - acc: 0.8174\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.5424 - acc: 0.8478\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.5065 - acc: 0.8870\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4936 - acc: 0.8826\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.5023 - acc: 0.8870\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.5092 - acc: 0.8609\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4992 - acc: 0.8652\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4684 - acc: 0.8783\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4793 - acc: 0.8783\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.4593 - acc: 0.8957\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.4344 - acc: 0.8957\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.4691 - acc: 0.8957\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4669 - acc: 0.8826\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4258 - acc: 0.9000\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.4218 - acc: 0.9043\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.4236 - acc: 0.9130\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4182 - acc: 0.8826\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.4373 - acc: 0.8826\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4367 - acc: 0.9130\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4416 - acc: 0.9130\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4303 - acc: 0.9130\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.4541 - acc: 0.9043\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.4113 - acc: 0.9043\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.3975 - acc: 0.9348\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.4145 - acc: 0.8913\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4240 - acc: 0.8957\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.4118 - acc: 0.9087\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3616 - acc: 0.9435\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3851 - acc: 0.9261\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4007 - acc: 0.9043\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3639 - acc: 0.9304\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3825 - acc: 0.9130\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.3280 - acc: 0.942 - 0s 396us/step - loss: 0.3445 - acc: 0.9391\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.3671 - acc: 0.9174\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3913 - acc: 0.9130\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3456 - acc: 0.9391\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3473 - acc: 0.9261\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3798 - acc: 0.9087\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3603 - acc: 0.9261\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.3569 - acc: 0.9174\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3761 - acc: 0.9391\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.3432 - acc: 0.9261\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.3364 - acc: 0.9435\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.3409 - acc: 0.9261\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3301 - acc: 0.9478\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3628 - acc: 0.9217\n",
      "Epoch 65/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3292 - acc: 0.9478\n",
      "Epoch 66/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3390 - acc: 0.9348\n",
      "Epoch 67/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.3176 - acc: 0.9435 0s - loss: 0.3313 - acc: 0.937\n",
      "Epoch 68/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3428 - acc: 0.9348\n",
      "Epoch 69/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3391 - acc: 0.9348\n",
      "Epoch 70/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3360 - acc: 0.9261\n",
      "Epoch 71/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3253 - acc: 0.9478\n",
      "Epoch 72/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3269 - acc: 0.9304\n",
      "Epoch 73/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3082 - acc: 0.9522\n",
      "Epoch 74/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3118 - acc: 0.9391\n",
      "Epoch 75/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3272 - acc: 0.9435\n",
      "Epoch 76/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2976 - acc: 0.9522\n",
      "Epoch 77/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3373 - acc: 0.9217\n",
      "Epoch 78/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3274 - acc: 0.9304\n",
      "Epoch 79/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3525 - acc: 0.9348\n",
      "Epoch 80/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3185 - acc: 0.9348\n",
      "Epoch 81/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3026 - acc: 0.9609\n",
      "Epoch 82/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3128 - acc: 0.9391\n",
      "Epoch 83/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 417us/step - loss: 0.3082 - acc: 0.9391\n",
      "Epoch 84/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3270 - acc: 0.9261\n",
      "Epoch 85/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3031 - acc: 0.9522\n",
      "Epoch 86/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.3144 - acc: 0.9435\n",
      "Epoch 87/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3036 - acc: 0.9391\n",
      "Epoch 88/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2779 - acc: 0.9609\n",
      "Epoch 89/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3023 - acc: 0.9435\n",
      "Epoch 90/300\n",
      "230/230 [==============================] - 0s 630us/step - loss: 0.2833 - acc: 0.9609\n",
      "Epoch 91/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.3100 - acc: 0.9391\n",
      "Epoch 92/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3067 - acc: 0.9348\n",
      "Epoch 93/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.2941 - acc: 0.9435\n",
      "Epoch 94/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3255 - acc: 0.9261\n",
      "Epoch 95/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2581 - acc: 0.9522\n",
      "Epoch 96/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2872 - acc: 0.9478\n",
      "Epoch 97/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.2837 - acc: 0.9435\n",
      "Epoch 98/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.2874 - acc: 0.9522\n",
      "Epoch 99/300\n",
      "230/230 [==============================] - 0s 870us/step - loss: 0.3129 - acc: 0.9217 0s - loss: 0.3172 - acc: 0.919\n",
      "Epoch 100/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2731 - acc: 0.9391\n",
      "Epoch 101/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2640 - acc: 0.9609\n",
      "Epoch 102/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2887 - acc: 0.9435\n",
      "Epoch 103/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2841 - acc: 0.9478\n",
      "Epoch 104/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2797 - acc: 0.9565\n",
      "Epoch 105/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2681 - acc: 0.9565\n",
      "Epoch 106/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2673 - acc: 0.9609\n",
      "Epoch 107/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2826 - acc: 0.9565\n",
      "Epoch 108/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.3363 - acc: 0.9261\n",
      "Epoch 109/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2657 - acc: 0.9565\n",
      "Epoch 110/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.2469 - acc: 0.9652\n",
      "Epoch 111/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2600 - acc: 0.9609\n",
      "Epoch 112/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2616 - acc: 0.9478\n",
      "Epoch 113/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2660 - acc: 0.9435\n",
      "Epoch 114/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2977 - acc: 0.9391\n",
      "Epoch 115/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2580 - acc: 0.9522\n",
      "Epoch 116/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2826 - acc: 0.9478\n",
      "Epoch 117/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2629 - acc: 0.9478\n",
      "Epoch 118/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2665 - acc: 0.9522\n",
      "Epoch 119/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2682 - acc: 0.9348\n",
      "Epoch 120/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2614 - acc: 0.9522\n",
      "Epoch 121/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2354 - acc: 0.9696\n",
      "Epoch 122/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2588 - acc: 0.9478\n",
      "Epoch 123/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2602 - acc: 0.9435\n",
      "Epoch 124/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2753 - acc: 0.9435\n",
      "Epoch 125/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2695 - acc: 0.9478\n",
      "Epoch 126/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2482 - acc: 0.9696\n",
      "Epoch 127/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2547 - acc: 0.9478\n",
      "Epoch 128/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2590 - acc: 0.9478\n",
      "Epoch 129/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2298 - acc: 0.9696\n",
      "Epoch 130/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2462 - acc: 0.9565\n",
      "Epoch 131/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2507 - acc: 0.9696\n",
      "Epoch 132/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2758 - acc: 0.9478\n",
      "Epoch 133/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2407 - acc: 0.9609\n",
      "Epoch 134/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2675 - acc: 0.9522\n",
      "Epoch 135/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2547 - acc: 0.9652\n",
      "Epoch 136/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2666 - acc: 0.9522\n",
      "Epoch 137/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2427 - acc: 0.9652\n",
      "Epoch 138/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2551 - acc: 0.9478\n",
      "Epoch 139/300\n",
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      "Epoch 140/300\n",
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      "Epoch 141/300\n",
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      "Epoch 142/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2534 - acc: 0.9522\n",
      "Epoch 143/300\n",
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      "Epoch 144/300\n",
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      "Epoch 145/300\n",
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      "Epoch 146/300\n",
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      "Epoch 147/300\n",
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      "Epoch 148/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2345 - acc: 0.9652\n",
      "Epoch 149/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2267 - acc: 0.9783\n",
      "Epoch 150/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2694 - acc: 0.9565 0s - loss: 0.2881 - acc: 0.950\n",
      "Epoch 151/300\n",
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      "Epoch 152/300\n",
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      "Epoch 153/300\n",
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      "Epoch 154/300\n",
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      "Epoch 155/300\n",
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      "Epoch 156/300\n",
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      "Epoch 157/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.2295 - acc: 0.9522\n",
      "Epoch 158/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2213 - acc: 0.9652\n",
      "Epoch 159/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2327 - acc: 0.9478\n",
      "Epoch 160/300\n",
      "230/230 [==============================] - 0s 444us/step - loss: 0.2372 - acc: 0.9696\n",
      "Epoch 161/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2362 - acc: 0.9565\n",
      "Epoch 162/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2348 - acc: 0.9609\n",
      "Epoch 163/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2358 - acc: 0.9565\n",
      "Epoch 164/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 457us/step - loss: 0.2316 - acc: 0.9522\n",
      "Epoch 165/300\n",
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      "Epoch 166/300\n",
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      "Epoch 167/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2415 - acc: 0.9565\n",
      "Epoch 168/300\n",
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      "Epoch 169/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2118 - acc: 0.9652\n",
      "Epoch 170/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2355 - acc: 0.9652\n",
      "Epoch 171/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2524 - acc: 0.9304\n",
      "Epoch 172/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.2186 - acc: 0.9652\n",
      "Epoch 173/300\n",
      "230/230 [==============================] - 0s 652us/step - loss: 0.2130 - acc: 0.9696\n",
      "Epoch 174/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.2151 - acc: 0.9565\n",
      "Epoch 175/300\n",
      "230/230 [==============================] - 0s 743us/step - loss: 0.2393 - acc: 0.9565\n",
      "Epoch 176/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2270 - acc: 0.9522\n",
      "Epoch 177/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2263 - acc: 0.9696\n",
      "Epoch 178/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2172 - acc: 0.9609\n",
      "Epoch 179/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.2226 - acc: 0.9565\n",
      "Epoch 180/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.2246 - acc: 0.9565\n",
      "Epoch 181/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2422 - acc: 0.9565\n",
      "Epoch 182/300\n",
      "230/230 [==============================] - 0s 774us/step - loss: 0.2060 - acc: 0.9826\n",
      "Epoch 183/300\n",
      "230/230 [==============================] - 0s 857us/step - loss: 0.2153 - acc: 0.9522\n",
      "Epoch 184/300\n",
      "230/230 [==============================] - 0s 561us/step - loss: 0.2244 - acc: 0.9478\n",
      "Epoch 185/300\n",
      "230/230 [==============================] - 0s 983us/step - loss: 0.2156 - acc: 0.9783\n",
      "Epoch 186/300\n",
      "230/230 [==============================] - 0s 922us/step - loss: 0.2097 - acc: 0.9739\n",
      "Epoch 187/300\n",
      "230/230 [==============================] - 0s 670us/step - loss: 0.2242 - acc: 0.9696\n",
      "Epoch 188/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2360 - acc: 0.9522\n",
      "Epoch 189/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2312 - acc: 0.9565\n",
      "Epoch 190/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1991 - acc: 0.9739\n",
      "Epoch 191/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.2273 - acc: 0.9522\n",
      "Epoch 192/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2038 - acc: 0.9783\n",
      "Epoch 193/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2092 - acc: 0.9696\n",
      "Epoch 194/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2108 - acc: 0.9609\n",
      "Epoch 195/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2181 - acc: 0.9609\n",
      "Epoch 196/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2077 - acc: 0.9652\n",
      "Epoch 197/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.1911 - acc: 0.9826\n",
      "Epoch 198/300\n",
      "230/230 [==============================] - 0s 648us/step - loss: 0.2032 - acc: 0.9652\n",
      "Epoch 199/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1967 - acc: 0.9739\n",
      "Epoch 200/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2231 - acc: 0.9522\n",
      "Epoch 201/300\n",
      "230/230 [==============================] - 0s 670us/step - loss: 0.2330 - acc: 0.9435\n",
      "Epoch 202/300\n",
      "230/230 [==============================] - 0s 626us/step - loss: 0.1996 - acc: 0.9739\n",
      "Epoch 203/300\n",
      "230/230 [==============================] - 0s 561us/step - loss: 0.1934 - acc: 0.9739\n",
      "Epoch 204/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2007 - acc: 0.9609\n",
      "Epoch 205/300\n",
      "230/230 [==============================] - 0s 561us/step - loss: 0.2012 - acc: 0.9696\n",
      "Epoch 206/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2140 - acc: 0.962 - 0s 530us/step - loss: 0.2173 - acc: 0.9609\n",
      "Epoch 207/300\n",
      "230/230 [==============================] - 0s 570us/step - loss: 0.2177 - acc: 0.9609\n",
      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 743us/step - loss: 0.2111 - acc: 0.9652\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.1856 - acc: 0.9826\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 726us/step - loss: 0.1875 - acc: 0.9826\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.1925 - acc: 0.9783\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1968 - acc: 0.9783\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2066 - acc: 0.9652\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.2120 - acc: 0.9652\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 670us/step - loss: 0.2154 - acc: 0.9522\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 657us/step - loss: 0.2156 - acc: 0.9696\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2285 - acc: 0.956 - 0s 574us/step - loss: 0.2249 - acc: 0.9565\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.2064 - acc: 0.9696\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2077 - acc: 0.9565\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 704us/step - loss: 0.1884 - acc: 0.9783\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - 0s 596us/step - loss: 0.2150 - acc: 0.9739\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 735us/step - loss: 0.1805 - acc: 0.9870\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1992 - acc: 0.9783\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2030 - acc: 0.9609\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1900 - acc: 0.9739\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1940 - acc: 0.9696\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1698 - acc: 0.9870\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1962 - acc: 0.9696\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1773 - acc: 0.9913\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1940 - acc: 0.9739\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2065 - acc: 0.9652\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1805 - acc: 0.9826\n",
      "Epoch 233/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1873 - acc: 0.9783\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2002 - acc: 0.9652\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1808 - acc: 0.9696\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1763 - acc: 0.9913\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1736 - acc: 0.9826\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1673 - acc: 0.9870\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1674 - acc: 0.9870\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1640 - acc: 0.9870\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.1966 - acc: 0.9696\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 683us/step - loss: 0.1882 - acc: 0.9696\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.1693 - acc: 0.9913\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 583us/step - loss: 0.1768 - acc: 0.9826\n",
      "Epoch 245/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 487us/step - loss: 0.1939 - acc: 0.9739\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1832 - acc: 0.9696\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1688 - acc: 0.9870\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1801 - acc: 0.9739\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1718 - acc: 0.9739\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1707 - acc: 0.9870\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.1799 - acc: 0.9739\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1695 - acc: 0.9870\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1763 - acc: 0.9652\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1745 - acc: 0.9696\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1833 - acc: 0.9696\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1911 - acc: 0.9696\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1682 - acc: 0.9826\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 522us/step - loss: 0.1695 - acc: 0.9826\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.1801 - acc: 0.9739\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 613us/step - loss: 0.1885 - acc: 0.9696\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.1797 - acc: 0.9826\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 726us/step - loss: 0.1699 - acc: 0.9783\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 687us/step - loss: 0.1725 - acc: 0.9826\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.1663 - acc: 0.9870\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 570us/step - loss: 0.1754 - acc: 0.9696\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 583us/step - loss: 0.1704 - acc: 0.9826\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 809us/step - loss: 0.1781 - acc: 0.9739\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 557us/step - loss: 0.1695 - acc: 0.9913\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1874 - acc: 0.9826\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.1921 - acc: 0.9783\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1701 - acc: 0.9826\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1752 - acc: 0.9696\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1783 - acc: 0.9783\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1765 - acc: 0.9826\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 600us/step - loss: 0.1663 - acc: 0.9826\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.1818 - acc: 0.9783\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1815 - acc: 0.9783\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1774 - acc: 0.9826\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 809us/step - loss: 0.1802 - acc: 0.9696\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1696 - acc: 0.9870\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1688 - acc: 0.9826\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.1797 - acc: 0.9783 0s - loss: 0.1750 - acc: 0.987\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1441 - acc: 0.9957\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.1840 - acc: 0.9609\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1788 - acc: 0.9739\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 630us/step - loss: 0.1634 - acc: 0.9783\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1632 - acc: 0.9783\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1607 - acc: 0.9870\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 1ms/step - loss: 0.1846 - acc: 0.9652\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 609us/step - loss: 0.1685 - acc: 0.9826\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.1799 - acc: 0.9783\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.1494 - acc: 0.9826\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1812 - acc: 0.9739\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 570us/step - loss: 0.1763 - acc: 0.9696\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.1724 - acc: 0.9652\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.1673 - acc: 0.9739\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1819 - acc: 0.9696\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1642 - acc: 0.9870\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1451 - acc: 0.984 - 0s 661us/step - loss: 0.1600 - acc: 0.9696\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 643us/step - loss: 0.1615 - acc: 0.9870\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 5s 22ms/step - loss: 1.4722 - acc: 0.4391\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 1.2394 - acc: 0.5478\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 1.0225 - acc: 0.5565\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.9693 - acc: 0.4957\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.9193 - acc: 0.5435\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 0.9088 - acc: 0.5609\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.8806 - acc: 0.5826\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.8403 - acc: 0.6739\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.8145 - acc: 0.7043\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.7896 - acc: 0.7261\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.7780 - acc: 0.7304\n",
      "Epoch 12/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 378us/step - loss: 0.7267 - acc: 0.7609\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.6978 - acc: 0.7957\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.6714 - acc: 0.8043\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.6260 - acc: 0.8174\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.6177 - acc: 0.8217\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.6336 - acc: 0.7957\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.5712 - acc: 0.8522\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.5609 - acc: 0.8565\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.5416 - acc: 0.8565\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5373 - acc: 0.8652\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.5060 - acc: 0.8609\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.5085 - acc: 0.8609\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.5154 - acc: 0.8696\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.4928 - acc: 0.8739\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4842 - acc: 0.8696\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4890 - acc: 0.8826 0s - loss: 0.4639 - acc: 0.901\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.4931 - acc: 0.8739\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4874 - acc: 0.8652\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4669 - acc: 0.8783\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4390 - acc: 0.8913\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4434 - acc: 0.9087\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4405 - acc: 0.8783\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.4572 - acc: 0.8783\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4819 - acc: 0.8783\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.4575 - acc: 0.8739\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4394 - acc: 0.8783\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4145 - acc: 0.9130\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4129 - acc: 0.9043\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4169 - acc: 0.8957\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3964 - acc: 0.9043\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3998 - acc: 0.9087\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4289 - acc: 0.9043\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3935 - acc: 0.9043\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.3989 - acc: 0.9000\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.4225 - acc: 0.9000 0s - loss: 0.4516 - acc: 0.885\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3969 - acc: 0.8913\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 0.4045 - acc: 0.9130\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3734 - acc: 0.9217\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3787 - acc: 0.9130\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3752 - acc: 0.9130 0s - loss: 0.3709 - acc: 0.921\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3749 - acc: 0.9087\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3740 - acc: 0.9000\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3596 - acc: 0.9391\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.3911 - acc: 0.9000\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.3533 - acc: 0.9304\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.3597 - acc: 0.9261\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.3666 - acc: 0.9174\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3610 - acc: 0.9217\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3430 - acc: 0.9304\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3311 - acc: 0.9217\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3365 - acc: 0.9391\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3576 - acc: 0.9174\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3591 - acc: 0.9261\n",
      "Epoch 65/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.3838 - acc: 0.9174 0s - loss: 0.4013 - acc: 0.911\n",
      "Epoch 66/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3390 - acc: 0.9348\n",
      "Epoch 67/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3717 - acc: 0.9043\n",
      "Epoch 68/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3421 - acc: 0.9217\n",
      "Epoch 69/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3153 - acc: 0.9435\n",
      "Epoch 70/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3759 - acc: 0.9130\n",
      "Epoch 71/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3643 - acc: 0.9174\n",
      "Epoch 72/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.3496 - acc: 0.9217\n",
      "Epoch 73/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3473 - acc: 0.9174\n",
      "Epoch 74/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 0.2937 - acc: 0.9435\n",
      "Epoch 75/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3436 - acc: 0.9174\n",
      "Epoch 76/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3043 - acc: 0.9261\n",
      "Epoch 77/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3186 - acc: 0.9348\n",
      "Epoch 78/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3076 - acc: 0.9435\n",
      "Epoch 79/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3088 - acc: 0.9435\n",
      "Epoch 80/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3142 - acc: 0.9478\n",
      "Epoch 81/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3158 - acc: 0.9435\n",
      "Epoch 82/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3121 - acc: 0.9522\n",
      "Epoch 83/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3082 - acc: 0.9217\n",
      "Epoch 84/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3160 - acc: 0.9261\n",
      "Epoch 85/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2944 - acc: 0.9435\n",
      "Epoch 86/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2941 - acc: 0.9261\n",
      "Epoch 87/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.3280 - acc: 0.9217\n",
      "Epoch 88/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3066 - acc: 0.9304\n",
      "Epoch 89/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2632 - acc: 0.9478\n",
      "Epoch 90/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3187 - acc: 0.9348\n",
      "Epoch 91/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3173 - acc: 0.9304\n",
      "Epoch 92/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3099 - acc: 0.9261\n",
      "Epoch 93/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 409us/step - loss: 0.2965 - acc: 0.9522\n",
      "Epoch 94/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3044 - acc: 0.9304\n",
      "Epoch 95/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2934 - acc: 0.9609\n",
      "Epoch 96/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2785 - acc: 0.9478\n",
      "Epoch 97/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2923 - acc: 0.9391\n",
      "Epoch 98/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2996 - acc: 0.9348\n",
      "Epoch 99/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2929 - acc: 0.9478\n",
      "Epoch 100/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2902 - acc: 0.9478\n",
      "Epoch 101/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2772 - acc: 0.9522\n",
      "Epoch 102/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2921 - acc: 0.9478\n",
      "Epoch 103/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2823 - acc: 0.9435\n",
      "Epoch 104/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2790 - acc: 0.9522\n",
      "Epoch 105/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3258 - acc: 0.9261\n",
      "Epoch 106/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2802 - acc: 0.9565\n",
      "Epoch 107/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.3016 - acc: 0.9304\n",
      "Epoch 108/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2645 - acc: 0.9522\n",
      "Epoch 109/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2852 - acc: 0.9565\n",
      "Epoch 110/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2943 - acc: 0.9348\n",
      "Epoch 111/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2669 - acc: 0.9478\n",
      "Epoch 112/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2550 - acc: 0.9565\n",
      "Epoch 113/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2569 - acc: 0.9522\n",
      "Epoch 114/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2999 - acc: 0.9435\n",
      "Epoch 115/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2780 - acc: 0.9304\n",
      "Epoch 116/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2583 - acc: 0.9609\n",
      "Epoch 117/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.2607 - acc: 0.9435\n",
      "Epoch 118/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2701 - acc: 0.9391\n",
      "Epoch 119/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2992 - acc: 0.9478\n",
      "Epoch 120/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2789 - acc: 0.9478\n",
      "Epoch 121/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2700 - acc: 0.9348\n",
      "Epoch 122/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2830 - acc: 0.9435\n",
      "Epoch 123/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2645 - acc: 0.9391\n",
      "Epoch 124/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2575 - acc: 0.9565\n",
      "Epoch 125/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2436 - acc: 0.9696\n",
      "Epoch 126/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2592 - acc: 0.9478\n",
      "Epoch 127/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2539 - acc: 0.9609\n",
      "Epoch 128/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2492 - acc: 0.9522\n",
      "Epoch 129/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2499 - acc: 0.9435\n",
      "Epoch 130/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2457 - acc: 0.9522\n",
      "Epoch 131/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2361 - acc: 0.9565\n",
      "Epoch 132/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2701 - acc: 0.9478\n",
      "Epoch 133/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2442 - acc: 0.9696\n",
      "Epoch 134/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2423 - acc: 0.9565\n",
      "Epoch 135/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2426 - acc: 0.9522 0s - loss: 0.2422 - acc: 0.953\n",
      "Epoch 136/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2404 - acc: 0.9435\n",
      "Epoch 137/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2806 - acc: 0.9348\n",
      "Epoch 138/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2565 - acc: 0.9522\n",
      "Epoch 139/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2355 - acc: 0.9652\n",
      "Epoch 140/300\n",
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      "Epoch 141/300\n",
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      "Epoch 142/300\n",
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      "Epoch 143/300\n",
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      "Epoch 144/300\n",
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      "Epoch 145/300\n",
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      "Epoch 146/300\n",
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      "Epoch 147/300\n",
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      "Epoch 148/300\n",
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      "Epoch 149/300\n",
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      "Epoch 150/300\n",
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      "Epoch 151/300\n",
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      "Epoch 152/300\n",
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      "Epoch 153/300\n",
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      "Epoch 155/300\n",
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      "Epoch 156/300\n",
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      "Epoch 158/300\n",
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      "Epoch 159/300\n",
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      "Epoch 160/300\n",
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      "Epoch 161/300\n",
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      "Epoch 162/300\n",
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      "Epoch 163/300\n",
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      "Epoch 164/300\n",
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      "Epoch 165/300\n",
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      "Epoch 166/300\n",
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      "Epoch 167/300\n",
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      "Epoch 168/300\n",
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      "Epoch 169/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2609 - acc: 0.9435\n",
      "Epoch 170/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2516 - acc: 0.9652\n",
      "Epoch 171/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2443 - acc: 0.9609\n",
      "Epoch 172/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2182 - acc: 0.9739\n",
      "Epoch 173/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2338 - acc: 0.9696\n",
      "Epoch 174/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 387us/step - loss: 0.2195 - acc: 0.9696\n",
      "Epoch 175/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2639 - acc: 0.9609\n",
      "Epoch 176/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2108 - acc: 0.9696\n",
      "Epoch 177/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2303 - acc: 0.9609\n",
      "Epoch 178/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2178 - acc: 0.9565\n",
      "Epoch 179/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2276 - acc: 0.9609\n",
      "Epoch 180/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2269 - acc: 0.9609\n",
      "Epoch 181/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2235 - acc: 0.9652\n",
      "Epoch 182/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2420 - acc: 0.9478\n",
      "Epoch 183/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2200 - acc: 0.9739\n",
      "Epoch 184/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2199 - acc: 0.9739\n",
      "Epoch 185/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2385 - acc: 0.9565\n",
      "Epoch 186/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2384 - acc: 0.9522\n",
      "Epoch 187/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2285 - acc: 0.9609\n",
      "Epoch 188/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2210 - acc: 0.9609\n",
      "Epoch 189/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2096 - acc: 0.9652\n",
      "Epoch 190/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2083 - acc: 0.9783\n",
      "Epoch 191/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2313 - acc: 0.9478\n",
      "Epoch 192/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1970 - acc: 0.9696\n",
      "Epoch 193/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2014 - acc: 0.9696\n",
      "Epoch 194/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2019 - acc: 0.9739\n",
      "Epoch 195/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2255 - acc: 0.9609\n",
      "Epoch 196/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2015 - acc: 0.9652\n",
      "Epoch 197/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2048 - acc: 0.9696\n",
      "Epoch 198/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1991 - acc: 0.9783\n",
      "Epoch 199/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2299 - acc: 0.9435\n",
      "Epoch 200/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2525 - acc: 0.9435\n",
      "Epoch 201/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2323 - acc: 0.9565\n",
      "Epoch 202/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1977 - acc: 0.9783\n",
      "Epoch 203/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2202 - acc: 0.9652\n",
      "Epoch 204/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2545 - acc: 0.9522\n",
      "Epoch 205/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1970 - acc: 0.9783\n",
      "Epoch 206/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2257 - acc: 0.9652\n",
      "Epoch 207/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2047 - acc: 0.9609\n",
      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1838 - acc: 0.9826\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2223 - acc: 0.9609\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2287 - acc: 0.9478\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2237 - acc: 0.9565\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2010 - acc: 0.9696\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1885 - acc: 0.9739\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1903 - acc: 0.9826\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2033 - acc: 0.9609\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2067 - acc: 0.9609\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1957 - acc: 0.9609\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2085 - acc: 0.9565\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2049 - acc: 0.9696\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2150 - acc: 0.9652\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2030 - acc: 0.9783\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1986 - acc: 0.9609\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2114 - acc: 0.9609\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1966 - acc: 0.9696\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2168 - acc: 0.9696\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1970 - acc: 0.9826\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1829 - acc: 0.9739\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2026 - acc: 0.9652\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2219 - acc: 0.9609\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2064 - acc: 0.9652\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2040 - acc: 0.9783\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1881 - acc: 0.9696\n",
      "Epoch 233/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2080 - acc: 0.9652\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1970 - acc: 0.9826\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1916 - acc: 0.9739\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2175 - acc: 0.9565\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2085 - acc: 0.9652\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2007 - acc: 0.9696\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2014 - acc: 0.9609\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1920 - acc: 0.9696\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1881 - acc: 0.9739\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1998 - acc: 0.9652\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1820 - acc: 0.9913\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1975 - acc: 0.9739\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2045 - acc: 0.9652\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1945 - acc: 0.9739\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1993 - acc: 0.9783\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1846 - acc: 0.9826\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1820 - acc: 0.9739\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1948 - acc: 0.9652\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2018 - acc: 0.9696\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2113 - acc: 0.9609\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1940 - acc: 0.9739\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1712 - acc: 0.9957\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2060 - acc: 0.9739\n",
      "Epoch 256/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 448us/step - loss: 0.2068 - acc: 0.9609\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1830 - acc: 0.9826\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1808 - acc: 0.9696\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1830 - acc: 0.9826\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1947 - acc: 0.9783\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2017 - acc: 0.9696\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1793 - acc: 0.9739 0s - loss: 0.1739 - acc: 0.979\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1925 - acc: 0.9696\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1883 - acc: 0.9696\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2022 - acc: 0.953 - 0s 400us/step - loss: 0.1944 - acc: 0.9609\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1805 - acc: 0.9783\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1795 - acc: 0.9739\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1770 - acc: 0.9783\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1866 - acc: 0.9609\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1867 - acc: 0.9652\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1966 - acc: 0.9696\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1783 - acc: 0.9696\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1753 - acc: 0.9826\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2263 - acc: 0.9522\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1978 - acc: 0.9609\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1756 - acc: 0.9783\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1913 - acc: 0.9739\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1705 - acc: 0.9870\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1805 - acc: 0.9696\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1595 - acc: 0.9957\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1826 - acc: 0.9739\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1845 - acc: 0.9783\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2080 - acc: 0.9609\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1956 - acc: 0.9739\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1825 - acc: 0.9783\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1780 - acc: 0.9826\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1986 - acc: 0.9522\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1807 - acc: 0.9652\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1735 - acc: 0.9739\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1668 - acc: 0.9783\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1730 - acc: 0.9783\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1811 - acc: 0.9913\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1642 - acc: 0.9826\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1881 - acc: 0.9609\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1747 - acc: 0.9826\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1991 - acc: 0.9696\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1491 - acc: 0.9870\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1560 - acc: 0.9826\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1821 - acc: 0.9739\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1579 - acc: 0.9913\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 5s 22ms/step - loss: 1.4758 - acc: 0.4217\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 1.2539 - acc: 0.5348\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 1.0744 - acc: 0.5609\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.9759 - acc: 0.5391\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.9434 - acc: 0.6130\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.9432 - acc: 0.5565\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.8851 - acc: 0.593 - 0s 413us/step - loss: 0.9093 - acc: 0.5913\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.8619 - acc: 0.6435\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.8466 - acc: 0.7000\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.8159 - acc: 0.7130\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.7843 - acc: 0.7435\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.7256 - acc: 0.7565\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.6991 - acc: 0.8130\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.6889 - acc: 0.7609\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.6306 - acc: 0.8261\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.6363 - acc: 0.8174\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.5885 - acc: 0.8435\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5785 - acc: 0.8478\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.5774 - acc: 0.8522\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5352 - acc: 0.8652\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.5370 - acc: 0.8565\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.5257 - acc: 0.8696\n",
      "Epoch 23/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 396us/step - loss: 0.5469 - acc: 0.8565\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4942 - acc: 0.8957\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4900 - acc: 0.8783\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5036 - acc: 0.8565\n",
      "Epoch 27/300\n",
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      "Epoch 202/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2024 - acc: 0.9739\n",
      "Epoch 203/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2460 - acc: 0.9522\n",
      "Epoch 204/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2128 - acc: 0.9696\n",
      "Epoch 205/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2137 - acc: 0.9652\n",
      "Epoch 206/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2195 - acc: 0.9652\n",
      "Epoch 207/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1919 - acc: 0.9826\n",
      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2220 - acc: 0.9652\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2119 - acc: 0.9696\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2119 - acc: 0.9696\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2095 - acc: 0.9739\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2121 - acc: 0.9739\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2177 - acc: 0.9696\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2110 - acc: 0.9696\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2229 - acc: 0.9652\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1963 - acc: 0.9739\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2085 - acc: 0.9609\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 0.1894 - acc: 0.9739\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2224 - acc: 0.9609\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1954 - acc: 0.9783\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2211 - acc: 0.9565\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2031 - acc: 0.9652\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1864 - acc: 0.9870\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2222 - acc: 0.9565\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.2300 - acc: 0.9435\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2082 - acc: 0.9652\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.2047 - acc: 0.9783\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2081 - acc: 0.9739\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1920 - acc: 0.9826\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2321 - acc: 0.9565\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1968 - acc: 0.9826\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1847 - acc: 0.9826\n",
      "Epoch 233/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1940 - acc: 0.9739\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1880 - acc: 0.9783\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1866 - acc: 0.9870\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1940 - acc: 0.9783\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1946 - acc: 0.9739\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1826 - acc: 0.9696\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1913 - acc: 0.9783\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2032 - acc: 0.9696\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2083 - acc: 0.9609\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2118 - acc: 0.9739\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1754 - acc: 0.9783\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2032 - acc: 0.9652\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2227 - acc: 0.9522\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1840 - acc: 0.9783\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1848 - acc: 0.9739\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1866 - acc: 0.9696\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2037 - acc: 0.9652\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1844 - acc: 0.9783\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2097 - acc: 0.9652\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1743 - acc: 0.9826\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1993 - acc: 0.9652\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1789 - acc: 0.9826\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1979 - acc: 0.9652 0s - loss: 0.2001 - acc: 0.963\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1940 - acc: 0.9696\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1796 - acc: 0.9826\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1774 - acc: 0.9783\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1778 - acc: 0.9870\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1904 - acc: 0.9696\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1626 - acc: 0.9957\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2066 - acc: 0.9565\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1724 - acc: 0.9783\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2218 - acc: 0.9652\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2072 - acc: 0.9609\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1821 - acc: 0.9870\n",
      "Epoch 267/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 391us/step - loss: 0.1999 - acc: 0.9652\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1790 - acc: 0.9783\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1903 - acc: 0.9609\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2068 - acc: 0.9696\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1980 - acc: 0.9652\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1811 - acc: 0.9826\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1947 - acc: 0.9696\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1849 - acc: 0.9696\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1754 - acc: 0.9870\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1841 - acc: 0.9826\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1933 - acc: 0.9652\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1738 - acc: 0.9870\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1742 - acc: 0.9826\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1622 - acc: 0.9826\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1835 - acc: 0.9696\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1664 - acc: 0.9783\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1810 - acc: 0.9739\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 370us/step - loss: 0.1779 - acc: 0.9870\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1741 - acc: 0.9739\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2055 - acc: 0.9739\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1546 - acc: 0.9913\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1670 - acc: 0.9870\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1793 - acc: 0.9783\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1836 - acc: 0.9652\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1785 - acc: 0.9783\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1563 - acc: 0.9913\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1926 - acc: 0.9739\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1774 - acc: 0.9783\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1902 - acc: 0.9739\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1898 - acc: 0.9696\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1650 - acc: 0.9913\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1693 - acc: 0.9783\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1712 - acc: 0.9739\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.1786 - acc: 0.9739\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 5s 23ms/step - loss: 1.4679 - acc: 0.4696\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 1.2288 - acc: 0.5348\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 1.0408 - acc: 0.5391\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.9640 - acc: 0.5783\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.9258 - acc: 0.5826\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.8866 - acc: 0.6174\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.8675 - acc: 0.6261 0s - loss: 0.8696 - acc: 0.630\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.8443 - acc: 0.6435\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.7834 - acc: 0.7130\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.7842 - acc: 0.7043 0s - loss: 0.7638 - acc: 0.703\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.7684 - acc: 0.7130\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.7621 - acc: 0.6826\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.6950 - acc: 0.7435\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.7004 - acc: 0.7652\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.6817 - acc: 0.7783\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.6707 - acc: 0.7478\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.6649 - acc: 0.7783\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.6170 - acc: 0.7870\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.6138 - acc: 0.7870\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.6175 - acc: 0.8174\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.6289 - acc: 0.8130\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.5993 - acc: 0.7957\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.5748 - acc: 0.8261\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.5517 - acc: 0.8130\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5447 - acc: 0.8522\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5589 - acc: 0.8435\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.5546 - acc: 0.8217\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5553 - acc: 0.8174\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4875 - acc: 0.8478\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4922 - acc: 0.8478\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.5337 - acc: 0.8304\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4980 - acc: 0.8565\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.5163 - acc: 0.8652\n",
      "Epoch 34/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 374us/step - loss: 0.4771 - acc: 0.8739\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.5155 - acc: 0.8478\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.4707 - acc: 0.8652\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.5034 - acc: 0.8652\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4873 - acc: 0.8609\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4780 - acc: 0.8652\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4775 - acc: 0.8826\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4586 - acc: 0.8522\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.5057 - acc: 0.8478\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4442 - acc: 0.8652\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.4624 - acc: 0.8652\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4703 - acc: 0.8652\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4581 - acc: 0.8783\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4462 - acc: 0.8783\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4642 - acc: 0.8870\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4482 - acc: 0.8739\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4196 - acc: 0.8957\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4403 - acc: 0.8783\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4296 - acc: 0.9000\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4163 - acc: 0.8826\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4167 - acc: 0.8870\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4133 - acc: 0.9043\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4330 - acc: 0.8783\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4222 - acc: 0.8957\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3665 - acc: 0.9261\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4315 - acc: 0.8826\n",
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      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2527 - acc: 0.9609\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2298 - acc: 0.9609\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2447 - acc: 0.9696\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2229 - acc: 0.9696\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2343 - acc: 0.9652\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2337 - acc: 0.9522\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2031 - acc: 0.9739\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2070 - acc: 0.9696\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2203 - acc: 0.9696\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2011 - acc: 0.9696\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2296 - acc: 0.9696\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2412 - acc: 0.9696\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2257 - acc: 0.962 - 0s 448us/step - loss: 0.2313 - acc: 0.9609\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2244 - acc: 0.9609\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2582 - acc: 0.9522\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2204 - acc: 0.9696\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2264 - acc: 0.9609\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1910 - acc: 0.9870\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2139 - acc: 0.9696\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.2142 - acc: 0.9783\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2197 - acc: 0.9652\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2431 - acc: 0.9696\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2179 - acc: 0.9783\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2183 - acc: 0.9696\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1976 - acc: 0.9783\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2413 - acc: 0.9565\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2197 - acc: 0.9609\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2246 - acc: 0.9696\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1951 - acc: 0.984 - 0s 435us/step - loss: 0.1897 - acc: 0.9870\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1867 - acc: 0.9913\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1871 - acc: 0.9783\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2142 - acc: 0.9739\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 639us/step - loss: 0.2421 - acc: 0.9565\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1937 - acc: 0.9783\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1941 - acc: 0.9826\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1837 - acc: 0.9870\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2178 - acc: 0.9609\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2001 - acc: 0.9652\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2069 - acc: 0.9783 0s - loss: 0.2094 - acc: 0.974\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1854 - acc: 0.9870\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1858 - acc: 0.9826\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2068 - acc: 0.9565\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.1986 - acc: 0.9826\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2053 - acc: 0.9609\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1999 - acc: 0.9696\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1883 - acc: 0.9826\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2117 - acc: 0.9739\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2040 - acc: 0.9696\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1804 - acc: 0.9826\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1837 - acc: 0.9826\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2127 - acc: 0.9652\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2374 - acc: 0.9478 0s - loss: 0.2488 - acc: 0.942\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1876 - acc: 0.9826\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2055 - acc: 0.9609\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2036 - acc: 0.9696\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2063 - acc: 0.9739\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1905 - acc: 0.9739\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1788 - acc: 0.9826\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2047 - acc: 0.9696\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1979 - acc: 0.9696\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2036 - acc: 0.9652\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2034 - acc: 0.9652\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1906 - acc: 0.9783\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2039 - acc: 0.9739\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1831 - acc: 0.9913\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1808 - acc: 0.9826\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 7s 30ms/step - loss: 1.4877 - acc: 0.4696\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 1.2919 - acc: 0.5696\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 1.0878 - acc: 0.5391\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 600us/step - loss: 0.9969 - acc: 0.5217\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.9351 - acc: 0.6000\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.8844 - acc: 0.7043\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 600us/step - loss: 0.8659 - acc: 0.6522\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.8583 - acc: 0.7000\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 565us/step - loss: 0.8208 - acc: 0.7000\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.7674 - acc: 0.6870\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.7395 - acc: 0.7739\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.7003 - acc: 0.8130\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.7178 - acc: 0.7696\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.6707 - acc: 0.7957\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.6737 - acc: 0.8000\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.6186 - acc: 0.8304\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.6138 - acc: 0.8261\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.5870 - acc: 0.8087\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.6161 - acc: 0.8391\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.5730 - acc: 0.8304\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.5462 - acc: 0.8391\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.5684 - acc: 0.8217\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.5338 - acc: 0.8522\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.5142 - acc: 0.8609\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.5750 - acc: 0.8478\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.5043 - acc: 0.8652\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5197 - acc: 0.8826\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.5328 - acc: 0.8261\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4976 - acc: 0.8565\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4848 - acc: 0.8609\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4782 - acc: 0.8870\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4584 - acc: 0.8913\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4407 - acc: 0.8783\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.5270 - acc: 0.8652\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4925 - acc: 0.8696\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.4576 - acc: 0.875 - 0s 409us/step - loss: 0.4386 - acc: 0.8870\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4386 - acc: 0.9261\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4449 - acc: 0.8913\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.4228 - acc: 0.8870\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4557 - acc: 0.8783\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.4504 - acc: 0.8870\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.4422 - acc: 0.8783\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4149 - acc: 0.9000\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4342 - acc: 0.8957\n",
      "Epoch 45/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 426us/step - loss: 0.4325 - acc: 0.8870\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4236 - acc: 0.9000\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4319 - acc: 0.8870\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.4185 - acc: 0.9000\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4074 - acc: 0.8957\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4061 - acc: 0.9000\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3790 - acc: 0.9043\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3941 - acc: 0.8913\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4056 - acc: 0.9043\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3521 - acc: 0.9391\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3767 - acc: 0.9304\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4209 - acc: 0.9000\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3561 - acc: 0.9087\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 374us/step - loss: 0.3939 - acc: 0.9000\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3930 - acc: 0.9043\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4029 - acc: 0.9087\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3636 - acc: 0.9130\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3768 - acc: 0.9391\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3992 - acc: 0.9130\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.3760 - acc: 0.916 - 0s 391us/step - loss: 0.3801 - acc: 0.9174\n",
      "Epoch 65/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4363 - acc: 0.8913\n",
      "Epoch 66/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3716 - acc: 0.9130\n",
      "Epoch 67/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3469 - acc: 0.9261\n",
      "Epoch 68/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3555 - acc: 0.9043\n",
      "Epoch 69/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3680 - acc: 0.9174\n",
      "Epoch 70/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.3836 - acc: 0.9174\n",
      "Epoch 71/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3419 - acc: 0.9217\n",
      "Epoch 72/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.3138 - acc: 0.9391\n",
      "Epoch 73/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.3626 - acc: 0.9391\n",
      "Epoch 74/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.3289 - acc: 0.9217\n",
      "Epoch 75/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.3356 - acc: 0.9304\n",
      "Epoch 76/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3846 - acc: 0.9043\n",
      "Epoch 77/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3205 - acc: 0.9304\n",
      "Epoch 78/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3378 - acc: 0.9304\n",
      "Epoch 79/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3720 - acc: 0.9087\n",
      "Epoch 80/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.3333 - acc: 0.9261\n",
      "Epoch 81/300\n",
      "230/230 [==============================] - 0s 735us/step - loss: 0.3364 - acc: 0.9217\n",
      "Epoch 82/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.3970 - acc: 0.9000\n",
      "Epoch 83/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3411 - acc: 0.9130\n",
      "Epoch 84/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3429 - acc: 0.9261\n",
      "Epoch 85/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3580 - acc: 0.9261\n",
      "Epoch 86/300\n",
      "230/230 [==============================] - 0s 713us/step - loss: 0.3223 - acc: 0.9304\n",
      "Epoch 87/300\n",
      "230/230 [==============================] - 0s 796us/step - loss: 0.3593 - acc: 0.9043\n",
      "Epoch 88/300\n",
      "230/230 [==============================] - 0s 635us/step - loss: 0.3240 - acc: 0.9217\n",
      "Epoch 89/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.3466 - acc: 0.9261\n",
      "Epoch 90/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.3331 - acc: 0.9261\n",
      "Epoch 91/300\n",
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     ]
    },
    {
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     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\keras\\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.128000). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
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      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.2140 - acc: 0.9696\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2380 - acc: 0.9522\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2216 - acc: 0.9783\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2181 - acc: 0.9565\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1919 - acc: 0.9783\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1980 - acc: 0.9696\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2029 - acc: 0.9652\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1968 - acc: 0.9652\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2270 - acc: 0.9609\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2108 - acc: 0.9739\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1933 - acc: 0.9783\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2019 - acc: 0.9739\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2219 - acc: 0.9522\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1957 - acc: 0.9696\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2160 - acc: 0.9609\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 587us/step - loss: 0.2237 - acc: 0.9565 0s - loss: 0.2420 - acc: 0.943\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 674us/step - loss: 0.2037 - acc: 0.9826\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.1952 - acc: 0.9739\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2062 - acc: 0.9739\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2343 - acc: 0.9565\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1900 - acc: 0.9783\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.1934 - acc: 0.9783\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 709us/step - loss: 0.1998 - acc: 0.9696\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 678us/step - loss: 0.1952 - acc: 0.9739\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2053 - acc: 0.9696\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2040 - acc: 0.9522\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2240 - acc: 0.9391\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1911 - acc: 0.9739\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1887 - acc: 0.9739\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2002 - acc: 0.9652\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1713 - acc: 0.9913\n",
      "Epoch 291/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 704us/step - loss: 0.2308 - acc: 0.9609\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2096 - acc: 0.9696\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1917 - acc: 0.9783\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2141 - acc: 0.9652\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 661us/step - loss: 0.1901 - acc: 0.9783\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1953 - acc: 0.9783\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2150 - acc: 0.9522\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1828 - acc: 0.9739\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2106 - acc: 0.9696\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2045 - acc: 0.9696\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 6s 25ms/step - loss: 1.4803 - acc: 0.4174\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 1.2576 - acc: 0.5826\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 1.0544 - acc: 0.5609\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.9546 - acc: 0.5913\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.9132 - acc: 0.5609\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.8803 - acc: 0.6826\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.8505 - acc: 0.6739\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.8098 - acc: 0.6826 0s - loss: 0.8029 - acc: 0.703\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.7634 - acc: 0.7391\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.7391 - acc: 0.7783\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.6713 - acc: 0.8043\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.6823 - acc: 0.8000\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.6676 - acc: 0.8043\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.6022 - acc: 0.8696\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.5866 - acc: 0.8522\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.5625 - acc: 0.8435\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.5833 - acc: 0.8391\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5477 - acc: 0.8478\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.5409 - acc: 0.8478\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.5269 - acc: 0.8609\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5021 - acc: 0.8826\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5115 - acc: 0.8696\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5107 - acc: 0.8783\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.5120 - acc: 0.8652\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.4552 - acc: 0.8870\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4597 - acc: 0.8957\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4467 - acc: 0.9043\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4743 - acc: 0.8783\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.4509 - acc: 0.8913\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4391 - acc: 0.8870\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4212 - acc: 0.9043\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.4536 - acc: 0.8957\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 522us/step - loss: 0.4196 - acc: 0.8957 0s - loss: 0.4459 - acc: 0.881\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.4407 - acc: 0.8783\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.4424 - acc: 0.8913\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4271 - acc: 0.9130\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4107 - acc: 0.9087\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3763 - acc: 0.9130\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.3942 - acc: 0.9130\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4138 - acc: 0.9087\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.4127 - acc: 0.9174\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4193 - acc: 0.8957\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3921 - acc: 0.9043\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3837 - acc: 0.9043\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3684 - acc: 0.9174\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4087 - acc: 0.8957\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3714 - acc: 0.9174\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3754 - acc: 0.9217\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3907 - acc: 0.9000\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.3601 - acc: 0.9261\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3642 - acc: 0.9348\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3856 - acc: 0.9087\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3630 - acc: 0.9217\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.3351 - acc: 0.9261\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.3614 - acc: 0.9130\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3322 - acc: 0.9348\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3495 - acc: 0.9348\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3330 - acc: 0.9348\n",
      "Epoch 59/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 400us/step - loss: 0.3410 - acc: 0.9348\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.3367 - acc: 0.9304\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3361 - acc: 0.9435\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3462 - acc: 0.9217\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3485 - acc: 0.9174\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.3246 - acc: 0.9261\n",
      "Epoch 65/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3445 - acc: 0.9261\n",
      "Epoch 66/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3277 - acc: 0.9174\n",
      "Epoch 67/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3517 - acc: 0.9174\n",
      "Epoch 68/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3262 - acc: 0.9478\n",
      "Epoch 69/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3398 - acc: 0.9174\n",
      "Epoch 70/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3132 - acc: 0.9435\n",
      "Epoch 71/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3344 - acc: 0.9304\n",
      "Epoch 72/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.3172 - acc: 0.9348\n",
      "Epoch 73/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3327 - acc: 0.9391\n",
      "Epoch 74/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2844 - acc: 0.9522\n",
      "Epoch 75/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.3212 - acc: 0.9435\n",
      "Epoch 76/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3069 - acc: 0.9435\n",
      "Epoch 77/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.3010 - acc: 0.9478\n",
      "Epoch 78/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2971 - acc: 0.9304\n",
      "Epoch 79/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3181 - acc: 0.9391\n",
      "Epoch 80/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.3221 - acc: 0.9478\n",
      "Epoch 81/300\n",
      "230/230 [==============================] - 0s 587us/step - loss: 0.3046 - acc: 0.9478\n",
      "Epoch 82/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2920 - acc: 0.9435\n",
      "Epoch 83/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2898 - acc: 0.9522\n",
      "Epoch 84/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2844 - acc: 0.9565\n",
      "Epoch 85/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3229 - acc: 0.9217\n",
      "Epoch 86/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2884 - acc: 0.9435\n",
      "Epoch 87/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2788 - acc: 0.9565\n",
      "Epoch 88/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2885 - acc: 0.9348\n",
      "Epoch 89/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2837 - acc: 0.931 - 0s 448us/step - loss: 0.2636 - acc: 0.9478\n",
      "Epoch 90/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3092 - acc: 0.9348\n",
      "Epoch 91/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2941 - acc: 0.9478\n",
      "Epoch 92/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2894 - acc: 0.9478\n",
      "Epoch 93/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2837 - acc: 0.9522\n",
      "Epoch 94/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.3024 - acc: 0.9565\n",
      "Epoch 95/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2722 - acc: 0.9435\n",
      "Epoch 96/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2932 - acc: 0.9261\n",
      "Epoch 97/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2544 - acc: 0.9609\n",
      "Epoch 98/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2803 - acc: 0.9435\n",
      "Epoch 99/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2491 - acc: 0.9609\n",
      "Epoch 100/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2780 - acc: 0.9565\n",
      "Epoch 101/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2720 - acc: 0.9565\n",
      "Epoch 102/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2595 - acc: 0.9696\n",
      "Epoch 103/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2567 - acc: 0.9522\n",
      "Epoch 104/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2562 - acc: 0.9565\n",
      "Epoch 105/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2777 - acc: 0.9478\n",
      "Epoch 106/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2992 - acc: 0.9435\n",
      "Epoch 107/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2591 - acc: 0.9522\n",
      "Epoch 108/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2618 - acc: 0.9565\n",
      "Epoch 109/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3037 - acc: 0.9391\n",
      "Epoch 110/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2428 - acc: 0.9739\n",
      "Epoch 111/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2552 - acc: 0.9522\n",
      "Epoch 112/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2644 - acc: 0.9652\n",
      "Epoch 113/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2448 - acc: 0.9609\n",
      "Epoch 114/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.2662 - acc: 0.9478\n",
      "Epoch 115/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2858 - acc: 0.9435\n",
      "Epoch 116/300\n",
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      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.1411 - acc: 1.0000\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1963 - acc: 0.9565\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1508 - acc: 0.9870\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.1615 - acc: 0.9826\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1470 - acc: 0.9913\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1651 - acc: 0.9870\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.1859 - acc: 0.9696\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1639 - acc: 0.9826\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 674us/step - loss: 0.1646 - acc: 0.9870\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1978 - acc: 0.968 - 0s 565us/step - loss: 0.1913 - acc: 0.9696\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 7s 29ms/step - loss: 1.4729 - acc: 0.4957\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 1.2570 - acc: 0.5348\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 1.0717 - acc: 0.5174\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.9537 - acc: 0.5565\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.9277 - acc: 0.5913 0s - loss: 0.9343 - acc: 0.593\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.8739 - acc: 0.6826\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.8647 - acc: 0.6217\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.8536 - acc: 0.6522\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.7884 - acc: 0.7217\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.7461 - acc: 0.7217\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.7260 - acc: 0.7217\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.6804 - acc: 0.7783\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.6328 - acc: 0.8348 0s - loss: 0.6273 - acc: 0.838\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.6430 - acc: 0.8043\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.6114 - acc: 0.8522\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.5919 - acc: 0.8522\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.6228 - acc: 0.8130\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.5867 - acc: 0.8130\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.5181 - acc: 0.8652\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5289 - acc: 0.8565\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.5136 - acc: 0.8739\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.5035 - acc: 0.8783\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4814 - acc: 0.9000\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4904 - acc: 0.8739\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4494 - acc: 0.8870\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4999 - acc: 0.8522\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.4570 - acc: 0.9174\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4534 - acc: 0.8783\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4354 - acc: 0.8957\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.4594 - acc: 0.8913\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 565us/step - loss: 0.4345 - acc: 0.9087\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 626us/step - loss: 0.4355 - acc: 0.9261\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 626us/step - loss: 0.4201 - acc: 0.9000\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.3986 - acc: 0.9087\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.4191 - acc: 0.9000\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.4056 - acc: 0.9174\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.4232 - acc: 0.9000\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3671 - acc: 0.9304\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3908 - acc: 0.9304\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4059 - acc: 0.9087\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3732 - acc: 0.9304\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 622us/step - loss: 0.4116 - acc: 0.9043\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 722us/step - loss: 0.4034 - acc: 0.9000\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 544us/step - loss: 0.3704 - acc: 0.9348\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3637 - acc: 0.9130\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.3765 - acc: 0.9087\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.3931 - acc: 0.9043\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.3661 - acc: 0.9478\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3530 - acc: 0.9304\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3495 - acc: 0.9391\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.3512 - acc: 0.9304\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.3249 - acc: 0.9348\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.3686 - acc: 0.9435\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3536 - acc: 0.9261\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.3240 - acc: 0.9391\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.3285 - acc: 0.9348\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.3393 - acc: 0.9174\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.3552 - acc: 0.9217\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.3077 - acc: 0.9348\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3283 - acc: 0.9435\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3245 - acc: 0.9478\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.3195 - acc: 0.9478\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3111 - acc: 0.9391\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2968 - acc: 0.9435\n",
      "Epoch 65/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.3127 - acc: 0.9478\n",
      "Epoch 66/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.3113 - acc: 0.9522\n",
      "Epoch 67/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.3233 - acc: 0.9391\n",
      "Epoch 68/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3245 - acc: 0.9304\n",
      "Epoch 69/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.3161 - acc: 0.9435\n",
      "Epoch 70/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 743us/step - loss: 0.3157 - acc: 0.9304\n",
      "Epoch 71/300\n",
      "230/230 [==============================] - 0s 648us/step - loss: 0.3075 - acc: 0.9435\n",
      "Epoch 72/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2926 - acc: 0.9435\n",
      "Epoch 73/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.3139 - acc: 0.9261\n",
      "Epoch 74/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.2750 - acc: 0.9652\n",
      "Epoch 75/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.3091 - acc: 0.9391\n",
      "Epoch 76/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2846 - acc: 0.9609\n",
      "Epoch 77/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2921 - acc: 0.9478\n",
      "Epoch 78/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2906 - acc: 0.9609\n",
      "Epoch 79/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.3148 - acc: 0.9565\n",
      "Epoch 80/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.2879 - acc: 0.9348\n",
      "Epoch 81/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2625 - acc: 0.9783\n",
      "Epoch 82/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2736 - acc: 0.9522\n",
      "Epoch 83/300\n",
      "230/230 [==============================] - 0s 444us/step - loss: 0.2738 - acc: 0.9696\n",
      "Epoch 84/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2902 - acc: 0.9435\n",
      "Epoch 85/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2800 - acc: 0.9696\n",
      "Epoch 86/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2601 - acc: 0.9696\n",
      "Epoch 87/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2666 - acc: 0.9522\n",
      "Epoch 88/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2566 - acc: 0.9696\n",
      "Epoch 89/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2631 - acc: 0.9478\n",
      "Epoch 90/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2413 - acc: 0.9913\n",
      "Epoch 91/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2419 - acc: 0.9783\n",
      "Epoch 92/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2927 - acc: 0.9391\n",
      "Epoch 93/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2696 - acc: 0.9522\n",
      "Epoch 94/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2513 - acc: 0.9522\n",
      "Epoch 95/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2553 - acc: 0.9696\n",
      "Epoch 96/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2756 - acc: 0.9522\n",
      "Epoch 97/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2520 - acc: 0.9652\n",
      "Epoch 98/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2519 - acc: 0.9783\n",
      "Epoch 99/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2493 - acc: 0.9696\n",
      "Epoch 100/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.2523 - acc: 0.9565\n",
      "Epoch 101/300\n",
      "230/230 [==============================] - 0s 722us/step - loss: 0.2467 - acc: 0.9609\n",
      "Epoch 102/300\n",
      "230/230 [==============================] - 0s 600us/step - loss: 0.2535 - acc: 0.9565\n",
      "Epoch 103/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.2439 - acc: 0.9522\n",
      "Epoch 104/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2489 - acc: 0.9783\n",
      "Epoch 105/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2374 - acc: 0.9652\n",
      "Epoch 106/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2494 - acc: 0.9739\n",
      "Epoch 107/300\n",
      "230/230 [==============================] - 0s 600us/step - loss: 0.2453 - acc: 0.9565\n",
      "Epoch 108/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.2331 - acc: 0.9739\n",
      "Epoch 109/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2241 - acc: 0.9609\n",
      "Epoch 110/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2340 - acc: 0.9696\n",
      "Epoch 111/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2444 - acc: 0.9478\n",
      "Epoch 112/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.2364 - acc: 0.9652\n",
      "Epoch 113/300\n",
      "230/230 [==============================] - 0s 691us/step - loss: 0.2267 - acc: 0.9739\n",
      "Epoch 114/300\n",
      "230/230 [==============================] - 0s 704us/step - loss: 0.2403 - acc: 0.9696\n",
      "Epoch 115/300\n",
      "230/230 [==============================] - 0s 522us/step - loss: 0.2244 - acc: 0.9696\n",
      "Epoch 116/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2330 - acc: 0.9652\n",
      "Epoch 117/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.2318 - acc: 0.9696\n",
      "Epoch 118/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2250 - acc: 0.9696\n",
      "Epoch 119/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2250 - acc: 0.9739\n",
      "Epoch 120/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2225 - acc: 0.9696\n",
      "Epoch 121/300\n",
      "230/230 [==============================] - 0s 630us/step - loss: 0.2280 - acc: 0.9696\n",
      "Epoch 122/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2160 - acc: 0.9739\n",
      "Epoch 123/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2069 - acc: 0.9783\n",
      "Epoch 124/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2185 - acc: 0.9652\n",
      "Epoch 125/300\n",
      "230/230 [==============================] - 0s 591us/step - loss: 0.2275 - acc: 0.9696\n",
      "Epoch 126/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2689 - acc: 0.9522\n",
      "Epoch 127/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2039 - acc: 0.9783\n",
      "Epoch 128/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1992 - acc: 0.9783\n",
      "Epoch 129/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2033 - acc: 0.9783\n",
      "Epoch 130/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2075 - acc: 0.9652\n",
      "Epoch 131/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2147 - acc: 0.9826\n",
      "Epoch 132/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.2111 - acc: 0.9696\n",
      "Epoch 133/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2214 - acc: 0.9696\n",
      "Epoch 134/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2258 - acc: 0.9652\n",
      "Epoch 135/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2371 - acc: 0.9609\n",
      "Epoch 136/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2160 - acc: 0.9826\n",
      "Epoch 137/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2085 - acc: 0.9652\n",
      "Epoch 138/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2463 - acc: 0.9652\n",
      "Epoch 139/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2109 - acc: 0.9696\n",
      "Epoch 140/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2129 - acc: 0.9652\n",
      "Epoch 141/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2208 - acc: 0.9870\n",
      "Epoch 142/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2036 - acc: 0.9783\n",
      "Epoch 143/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1983 - acc: 0.9870\n",
      "Epoch 144/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2152 - acc: 0.9696\n",
      "Epoch 145/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1902 - acc: 0.984 - 0s 396us/step - loss: 0.1979 - acc: 0.9826\n",
      "Epoch 146/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1958 - acc: 0.9870\n",
      "Epoch 147/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2088 - acc: 0.9739\n",
      "Epoch 148/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1828 - acc: 0.9913\n",
      "Epoch 149/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2169 - acc: 0.9565\n",
      "Epoch 150/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2158 - acc: 0.9696\n",
      "Epoch 151/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1775 - acc: 0.9913\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 152/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2023 - acc: 0.9913\n",
      "Epoch 153/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.2002 - acc: 0.9739\n",
      "Epoch 154/300\n",
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      "Epoch 155/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2316 - acc: 0.9609\n",
      "Epoch 156/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1965 - acc: 0.9870\n",
      "Epoch 157/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.2202 - acc: 0.9609\n",
      "Epoch 158/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2041 - acc: 0.9783\n",
      "Epoch 159/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2014 - acc: 0.9783\n",
      "Epoch 160/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2080 - acc: 0.9739\n",
      "Epoch 161/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1931 - acc: 0.9826\n",
      "Epoch 162/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1899 - acc: 0.9739\n",
      "Epoch 163/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1831 - acc: 0.9783\n",
      "Epoch 164/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1821 - acc: 0.9826\n",
      "Epoch 165/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1972 - acc: 0.9739\n",
      "Epoch 166/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1888 - acc: 0.9826\n",
      "Epoch 167/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1812 - acc: 0.9913\n",
      "Epoch 168/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1807 - acc: 0.9913\n",
      "Epoch 169/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2019 - acc: 0.9696\n",
      "Epoch 170/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1932 - acc: 0.9783\n",
      "Epoch 171/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1791 - acc: 0.9870\n",
      "Epoch 172/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1951 - acc: 0.9739\n",
      "Epoch 173/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1936 - acc: 0.974 - 0s 391us/step - loss: 0.1881 - acc: 0.9783\n",
      "Epoch 174/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1846 - acc: 0.9826\n",
      "Epoch 175/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1830 - acc: 0.9783\n",
      "Epoch 176/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1996 - acc: 0.9739\n",
      "Epoch 177/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1896 - acc: 0.9826\n",
      "Epoch 178/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1864 - acc: 0.9870\n",
      "Epoch 179/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1822 - acc: 0.9739\n",
      "Epoch 180/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1900 - acc: 0.9783\n",
      "Epoch 181/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1821 - acc: 0.9783\n",
      "Epoch 182/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1882 - acc: 0.9826\n",
      "Epoch 183/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1892 - acc: 0.9826\n",
      "Epoch 184/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1779 - acc: 0.9783\n",
      "Epoch 185/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1664 - acc: 0.9913\n",
      "Epoch 186/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1709 - acc: 0.9870\n",
      "Epoch 187/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1692 - acc: 0.9826\n",
      "Epoch 188/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1887 - acc: 0.9826\n",
      "Epoch 189/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1637 - acc: 0.9913\n",
      "Epoch 190/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1954 - acc: 0.9565\n",
      "Epoch 191/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1764 - acc: 0.9739\n",
      "Epoch 192/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1926 - acc: 0.9652\n",
      "Epoch 193/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1639 - acc: 0.9913\n",
      "Epoch 194/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1913 - acc: 0.9696\n",
      "Epoch 195/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1662 - acc: 0.9826\n",
      "Epoch 196/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1668 - acc: 0.9870\n",
      "Epoch 197/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1893 - acc: 0.9739\n",
      "Epoch 198/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1847 - acc: 0.9783\n",
      "Epoch 199/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1978 - acc: 0.9739\n",
      "Epoch 200/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1974 - acc: 0.9652\n",
      "Epoch 201/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1877 - acc: 0.9783\n",
      "Epoch 202/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1837 - acc: 0.9783\n",
      "Epoch 203/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1947 - acc: 0.9609\n",
      "Epoch 204/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1519 - acc: 0.9957\n",
      "Epoch 205/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1806 - acc: 0.9739\n",
      "Epoch 206/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1745 - acc: 0.9826\n",
      "Epoch 207/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1864 - acc: 0.9870\n",
      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2113 - acc: 0.9609\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1813 - acc: 0.9739\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1784 - acc: 0.9739\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1629 - acc: 0.9913\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1891 - acc: 0.9783\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1718 - acc: 0.9870\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1906 - acc: 0.9696\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1913 - acc: 0.9696\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1565 - acc: 0.9957\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1712 - acc: 0.9870\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1644 - acc: 0.9913\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1523 - acc: 0.9957\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1841 - acc: 0.9826\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1650 - acc: 0.989 - 0s 400us/step - loss: 0.1623 - acc: 0.9913\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1625 - acc: 0.9826\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1562 - acc: 0.9913\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1633 - acc: 0.9826\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1619 - acc: 0.9826\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1620 - acc: 0.9826\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1697 - acc: 0.9783\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1486 - acc: 0.9870\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1698 - acc: 0.9870\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1471 - acc: 0.9870\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1851 - acc: 0.9739\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1568 - acc: 0.9870\n",
      "Epoch 233/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 422us/step - loss: 0.1493 - acc: 0.9913\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1528 - acc: 0.9870\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1504 - acc: 0.9957\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1692 - acc: 0.9783\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1593 - acc: 0.9870 0s - loss: 0.1609 - acc: 0.984\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1779 - acc: 0.964 - 0s 691us/step - loss: 0.1794 - acc: 0.9652\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 726us/step - loss: 0.1500 - acc: 0.9957\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 657us/step - loss: 0.1546 - acc: 0.9783\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.1858 - acc: 0.9609\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1424 - acc: 0.9913\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 913us/step - loss: 0.1798 - acc: 0.9783\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 517us/step - loss: 0.1540 - acc: 1.0000\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.1586 - acc: 0.9913\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1725 - acc: 0.9783\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1777 - acc: 0.9696\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1508 - acc: 0.9870\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1399 - acc: 0.9957\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1573 - acc: 0.9870\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1510 - acc: 0.9957\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1587 - acc: 0.9783\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1549 - acc: 0.9870\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1771 - acc: 0.9826\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1625 - acc: 0.9783\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1512 - acc: 0.9913\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1811 - acc: 0.9826\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1814 - acc: 0.9739\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1452 - acc: 0.9870\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1490 - acc: 0.9913\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1565 - acc: 0.9870\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1705 - acc: 0.9826\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1417 - acc: 0.9913\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1529 - acc: 0.9826\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1583 - acc: 0.9870\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1736 - acc: 0.9696\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.1530 - acc: 0.9870\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.1725 - acc: 0.9696\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.1492 - acc: 0.9870\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 565us/step - loss: 0.1428 - acc: 0.9957\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1446 - acc: 0.9870\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1653 - acc: 0.9652\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1462 - acc: 0.9913\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.1485 - acc: 0.9913\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.1671 - acc: 0.9826\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.1450 - acc: 0.9913\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1351 - acc: 0.9913\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1495 - acc: 0.9826\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1463 - acc: 0.9870\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1630 - acc: 0.9826\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1536 - acc: 0.9870\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1742 - acc: 0.9739\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1535 - acc: 0.9913\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1614 - acc: 0.9696\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1563 - acc: 0.9783\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1684 - acc: 0.9652\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1693 - acc: 0.9783\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1744 - acc: 0.9739\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1606 - acc: 0.9826\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1596 - acc: 0.9826\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1511 - acc: 0.9826\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1468 - acc: 0.9870\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1403 - acc: 0.9913\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1464 - acc: 0.9870\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1493 - acc: 0.9870\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1510 - acc: 0.9826\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1585 - acc: 0.9870\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1390 - acc: 0.9870\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1433 - acc: 0.9913\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1527 - acc: 0.9870\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 7s 29ms/step - loss: 1.4693 - acc: 0.4783\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 1.2380 - acc: 0.5174\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 1.0535 - acc: 0.5130\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.9616 - acc: 0.5435\n",
      "Epoch 5/300\n",
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      "230/230 [==============================] - 0s 422us/step - loss: 0.2398 - acc: 0.9478\n",
      "Epoch 178/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2415 - acc: 0.9565\n",
      "Epoch 179/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2009 - acc: 0.9913\n",
      "Epoch 180/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.2253 - acc: 0.9739\n",
      "Epoch 181/300\n",
      "230/230 [==============================] - 0s 509us/step - loss: 0.2145 - acc: 0.9696\n",
      "Epoch 182/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2601 - acc: 0.9478\n",
      "Epoch 183/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.2366 - acc: 0.9609\n",
      "Epoch 184/300\n",
      "230/230 [==============================] - 0s 870us/step - loss: 0.2074 - acc: 0.9739\n",
      "Epoch 185/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2059 - acc: 0.9696\n",
      "Epoch 186/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.2010 - acc: 0.9870\n",
      "Epoch 187/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.2569 - acc: 0.9522\n",
      "Epoch 188/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.2041 - acc: 0.968 - 0s 557us/step - loss: 0.2091 - acc: 0.9739\n",
      "Epoch 189/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2117 - acc: 0.9826\n",
      "Epoch 190/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2376 - acc: 0.9565\n",
      "Epoch 191/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.2202 - acc: 0.9652\n",
      "Epoch 192/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.2320 - acc: 0.9609\n",
      "Epoch 193/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2154 - acc: 0.9696\n",
      "Epoch 194/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.2105 - acc: 0.9783\n",
      "Epoch 195/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2192 - acc: 0.9783 0s - loss: 0.2286 - acc: 0.979\n",
      "Epoch 196/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1886 - acc: 0.9870\n",
      "Epoch 197/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.1961 - acc: 0.9870\n",
      "Epoch 198/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2100 - acc: 0.9739\n",
      "Epoch 199/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2053 - acc: 0.9652\n",
      "Epoch 200/300\n",
      "230/230 [==============================] - 0s 726us/step - loss: 0.2170 - acc: 0.9609 0s - loss: 0.2192 - acc: 0.959\n",
      "Epoch 201/300\n",
      "230/230 [==============================] - 0s 735us/step - loss: 0.2055 - acc: 0.9826\n",
      "Epoch 202/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2188 - acc: 0.9652\n",
      "Epoch 203/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2227 - acc: 0.9739\n",
      "Epoch 204/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2280 - acc: 0.9652\n",
      "Epoch 205/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2019 - acc: 0.9826\n",
      "Epoch 206/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2041 - acc: 0.9739\n",
      "Epoch 207/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2517 - acc: 0.9565\n",
      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1971 - acc: 0.9739\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1900 - acc: 0.9783\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1950 - acc: 0.9739\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2038 - acc: 0.9826\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2085 - acc: 0.9696\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2343 - acc: 0.9696\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2022 - acc: 0.9826\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1987 - acc: 0.9696\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2258 - acc: 0.9609\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2254 - acc: 0.9652\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2209 - acc: 0.9565\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2024 - acc: 0.9739\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1936 - acc: 0.9783\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2120 - acc: 0.9609\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2146 - acc: 0.9652\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1897 - acc: 0.9783\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2179 - acc: 0.9696\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2135 - acc: 0.9696\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1766 - acc: 0.9870\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2452 - acc: 0.9522\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1929 - acc: 0.9783\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2153 - acc: 0.9696\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2012 - acc: 0.9652\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2000 - acc: 0.9870\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2038 - acc: 0.9696\n",
      "Epoch 233/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2011 - acc: 0.9739\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1971 - acc: 0.9826\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1823 - acc: 0.9870\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1934 - acc: 0.9826\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.2004 - acc: 0.9609\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1713 - acc: 0.9870\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1866 - acc: 0.9783\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1967 - acc: 0.9783\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1817 - acc: 0.9783\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1705 - acc: 0.9913\n",
      "Epoch 243/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 426us/step - loss: 0.1926 - acc: 0.9826\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1705 - acc: 0.9870\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1849 - acc: 0.9783\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1662 - acc: 0.9870\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1905 - acc: 0.9870\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1863 - acc: 0.9739\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1989 - acc: 0.9609\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2036 - acc: 0.9739\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1971 - acc: 0.9652\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2073 - acc: 0.9739\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1906 - acc: 0.9739\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1889 - acc: 0.9870\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1967 - acc: 0.9739\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1790 - acc: 0.9826\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1950 - acc: 0.9739\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - ETA: 0s - loss: 0.1696 - acc: 1.000 - ETA: 0s - loss: 0.1861 - acc: 0.979 - 0s 422us/step - loss: 0.1850 - acc: 0.9826\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2065 - acc: 0.9652\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1708 - acc: 0.9870\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1982 - acc: 0.9739\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2034 - acc: 0.9652\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1891 - acc: 0.9609\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2113 - acc: 0.9696\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1945 - acc: 0.9696\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1566 - acc: 1.0000\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 418us/step - loss: 0.1746 - acc: 0.9783\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1870 - acc: 0.9739\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1893 - acc: 0.9826\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2100 - acc: 0.9609\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1882 - acc: 0.9783\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1666 - acc: 0.9826\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1947 - acc: 0.9652\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1811 - acc: 0.9826\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1868 - acc: 0.9783\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1789 - acc: 0.9696\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1905 - acc: 0.9696\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1620 - acc: 0.9826\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1845 - acc: 0.9826\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2016 - acc: 0.9739\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1714 - acc: 0.9870\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.1844 - acc: 0.9870\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.2097 - acc: 0.9696\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.1815 - acc: 0.9826\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1750 - acc: 0.9870\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.1934 - acc: 0.9609\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.2029 - acc: 0.9652\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1799 - acc: 0.9696\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1918 - acc: 0.9652\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1970 - acc: 0.9652\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1788 - acc: 0.9783\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.1749 - acc: 0.9783\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1873 - acc: 0.9783\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.1655 - acc: 0.9826\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2001 - acc: 0.9739 0s - loss: 0.2080 - acc: 0.968\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.1616 - acc: 0.9870\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.1855 - acc: 0.9739\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.2006 - acc: 0.9609\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.1756 - acc: 0.9783\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1659 - acc: 0.9870\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 6s 26ms/step - loss: 1.4821 - acc: 0.4652\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 1.2834 - acc: 0.5696\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 1.0848 - acc: 0.5652\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.9785 - acc: 0.5957\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.9386 - acc: 0.5565\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.9040 - acc: 0.6435\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.8828 - acc: 0.6435\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.8435 - acc: 0.7043\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.8038 - acc: 0.7391\n",
      "Epoch 10/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 400us/step - loss: 0.7812 - acc: 0.7304\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.7313 - acc: 0.7957\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.7073 - acc: 0.7913\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.6441 - acc: 0.8000\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.6269 - acc: 0.8522\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 431us/step - loss: 0.6281 - acc: 0.7913\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5681 - acc: 0.8391\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5901 - acc: 0.8304\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.5802 - acc: 0.8348 0s - loss: 0.5777 - acc: 0.833\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5182 - acc: 0.8609\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.5659 - acc: 0.8261\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.5278 - acc: 0.8609\n",
      "Epoch 22/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.5339 - acc: 0.8478\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.5307 - acc: 0.8565\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.4920 - acc: 0.8739\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 396us/step - loss: 0.4788 - acc: 0.8739\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.4747 - acc: 0.8870\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 383us/step - loss: 0.5197 - acc: 0.8478\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 378us/step - loss: 0.4678 - acc: 0.8870\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.4850 - acc: 0.8913\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.4839 - acc: 0.8957\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.4924 - acc: 0.8652\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 387us/step - loss: 0.4451 - acc: 0.8826\n",
      "Epoch 33/300\n",
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      "Epoch 207/300\n",
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      "Epoch 208/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2441 - acc: 0.9478\n",
      "Epoch 209/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2170 - acc: 0.9565\n",
      "Epoch 210/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2055 - acc: 0.9783\n",
      "Epoch 211/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2303 - acc: 0.9565\n",
      "Epoch 212/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.2340 - acc: 0.9565\n",
      "Epoch 213/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2409 - acc: 0.9435\n",
      "Epoch 214/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2484 - acc: 0.9435\n",
      "Epoch 215/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2223 - acc: 0.9609\n",
      "Epoch 216/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.2197 - acc: 0.9609\n",
      "Epoch 217/300\n",
      "230/230 [==============================] - 0s 557us/step - loss: 0.2266 - acc: 0.9696\n",
      "Epoch 218/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2102 - acc: 0.9696\n",
      "Epoch 219/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2195 - acc: 0.9652\n",
      "Epoch 220/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2325 - acc: 0.9609\n",
      "Epoch 221/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2224 - acc: 0.9739\n",
      "Epoch 222/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2154 - acc: 0.9783\n",
      "Epoch 223/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2113 - acc: 0.9565\n",
      "Epoch 224/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.2462 - acc: 0.9565\n",
      "Epoch 225/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2516 - acc: 0.9565\n",
      "Epoch 226/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2184 - acc: 0.9652\n",
      "Epoch 227/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2536 - acc: 0.9478\n",
      "Epoch 228/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2308 - acc: 0.9652\n",
      "Epoch 229/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1906 - acc: 0.9826\n",
      "Epoch 230/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2098 - acc: 0.9783\n",
      "Epoch 231/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2018 - acc: 0.9826\n",
      "Epoch 232/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.2211 - acc: 0.9652\n",
      "Epoch 233/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2281 - acc: 0.9522\n",
      "Epoch 234/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2012 - acc: 0.9565\n",
      "Epoch 235/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2195 - acc: 0.9739\n",
      "Epoch 236/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2156 - acc: 0.9696\n",
      "Epoch 237/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2233 - acc: 0.9609\n",
      "Epoch 238/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2041 - acc: 0.9783\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1909 - acc: 0.9783\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2013 - acc: 0.9826\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2150 - acc: 0.9652\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.2160 - acc: 0.9696\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2184 - acc: 0.9609\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2176 - acc: 0.9609\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2224 - acc: 0.9609\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2207 - acc: 0.9652\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2019 - acc: 0.9652\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 691us/step - loss: 0.2012 - acc: 0.9783\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.2172 - acc: 0.9652\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 700us/step - loss: 0.2185 - acc: 0.9696\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.2176 - acc: 0.9696\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1941 - acc: 0.9739\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.2232 - acc: 0.9609\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2048 - acc: 0.9739\n",
      "Epoch 255/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 543us/step - loss: 0.2077 - acc: 0.9696\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 717us/step - loss: 0.1996 - acc: 0.9652\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2147 - acc: 0.9652\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.1839 - acc: 0.9826\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 604us/step - loss: 0.1971 - acc: 0.9783\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 522us/step - loss: 0.2040 - acc: 0.9609\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 1ms/step - loss: 0.1654 - acc: 0.9957\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 722us/step - loss: 0.2077 - acc: 0.9696\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 674us/step - loss: 0.1910 - acc: 0.9783\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.2324 - acc: 0.9435\n",
      "Epoch 265/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1852 - acc: 0.9826\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.1858 - acc: 0.9826\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 657us/step - loss: 0.1828 - acc: 0.9826\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2045 - acc: 0.9739\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1945 - acc: 0.9783\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 417us/step - loss: 0.1887 - acc: 0.9783\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1950 - acc: 0.9826\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.1944 - acc: 0.9696\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 674us/step - loss: 0.2347 - acc: 0.9565\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1991 - acc: 0.9826\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2286 - acc: 0.9565\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 587us/step - loss: 0.1974 - acc: 0.9739\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1992 - acc: 0.9739\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1923 - acc: 0.9783\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2061 - acc: 0.9652\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2401 - acc: 0.9478\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.1816 - acc: 0.9826\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.2099 - acc: 0.9783\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1883 - acc: 0.9870\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2062 - acc: 0.9826\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1948 - acc: 0.9783\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.2169 - acc: 0.9609\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.1940 - acc: 0.9696\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 565us/step - loss: 0.2250 - acc: 0.9478 0s - loss: 0.1789 - acc: 0.968\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1905 - acc: 0.9913\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 535us/step - loss: 0.2006 - acc: 0.9652\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 456us/step - loss: 0.1737 - acc: 0.9783\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1904 - acc: 0.9696 0s - loss: 0.1915 - acc: 0.962\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2156 - acc: 0.9609\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.1928 - acc: 0.9652\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2162 - acc: 0.9522\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2104 - acc: 0.9739\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2254 - acc: 0.9609\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.1702 - acc: 0.9913\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.1933 - acc: 0.9739\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1970 - acc: 0.9696\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Epoch 1/300\n",
      "230/230 [==============================] - 6s 28ms/step - loss: 1.4697 - acc: 0.4217\n",
      "Epoch 2/300\n",
      "230/230 [==============================] - 0s 730us/step - loss: 1.2573 - acc: 0.4913\n",
      "Epoch 3/300\n",
      "230/230 [==============================] - 0s 839us/step - loss: 1.0697 - acc: 0.5174\n",
      "Epoch 4/300\n",
      "230/230 [==============================] - 0s 400us/step - loss: 0.9805 - acc: 0.5609\n",
      "Epoch 5/300\n",
      "230/230 [==============================] - 0s 391us/step - loss: 0.9330 - acc: 0.5435\n",
      "Epoch 6/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.9103 - acc: 0.6043\n",
      "Epoch 7/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.8873 - acc: 0.6130\n",
      "Epoch 8/300\n",
      "230/230 [==============================] - 0s 713us/step - loss: 0.8577 - acc: 0.6348\n",
      "Epoch 9/300\n",
      "230/230 [==============================] - 0s 661us/step - loss: 0.8616 - acc: 0.6261\n",
      "Epoch 10/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.8169 - acc: 0.6957\n",
      "Epoch 11/300\n",
      "230/230 [==============================] - 0s 583us/step - loss: 0.8173 - acc: 0.6696\n",
      "Epoch 12/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.7469 - acc: 0.7609\n",
      "Epoch 13/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.7464 - acc: 0.7391\n",
      "Epoch 14/300\n",
      "230/230 [==============================] - 0s 830us/step - loss: 0.7029 - acc: 0.7783\n",
      "Epoch 15/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.6841 - acc: 0.7826\n",
      "Epoch 16/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.6609 - acc: 0.8391\n",
      "Epoch 17/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.6482 - acc: 0.8087\n",
      "Epoch 18/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.6366 - acc: 0.8261\n",
      "Epoch 19/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.6146 - acc: 0.8261\n",
      "Epoch 20/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.5583 - acc: 0.8609\n",
      "Epoch 21/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.5770 - acc: 0.8478\n",
      "Epoch 22/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 639us/step - loss: 0.5707 - acc: 0.8522\n",
      "Epoch 23/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.5431 - acc: 0.8522\n",
      "Epoch 24/300\n",
      "230/230 [==============================] - 0s 839us/step - loss: 0.5613 - acc: 0.8696\n",
      "Epoch 25/300\n",
      "230/230 [==============================] - 0s 574us/step - loss: 0.5102 - acc: 0.8783\n",
      "Epoch 26/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.5429 - acc: 0.8565\n",
      "Epoch 27/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.5093 - acc: 0.8652\n",
      "Epoch 28/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.5413 - acc: 0.8348\n",
      "Epoch 29/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.4915 - acc: 0.8826\n",
      "Epoch 30/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.4995 - acc: 0.8609\n",
      "Epoch 31/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.5034 - acc: 0.8783\n",
      "Epoch 32/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.5145 - acc: 0.8652\n",
      "Epoch 33/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.4896 - acc: 0.8913\n",
      "Epoch 34/300\n",
      "230/230 [==============================] - 0s 570us/step - loss: 0.4856 - acc: 0.8478\n",
      "Epoch 35/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.4590 - acc: 0.8870\n",
      "Epoch 36/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.4644 - acc: 0.8870\n",
      "Epoch 37/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.4832 - acc: 0.8652\n",
      "Epoch 38/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.4845 - acc: 0.8652\n",
      "Epoch 39/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.4720 - acc: 0.8783\n",
      "Epoch 40/300\n",
      "230/230 [==============================] - 0s 522us/step - loss: 0.4466 - acc: 0.8870\n",
      "Epoch 41/300\n",
      "230/230 [==============================] - 0s 552us/step - loss: 0.4125 - acc: 0.9043\n",
      "Epoch 42/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.4398 - acc: 0.8870\n",
      "Epoch 43/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.4048 - acc: 0.9130\n",
      "Epoch 44/300\n",
      "230/230 [==============================] - 0s 596us/step - loss: 0.3922 - acc: 0.9217\n",
      "Epoch 45/300\n",
      "230/230 [==============================] - 0s 526us/step - loss: 0.4499 - acc: 0.8913\n",
      "Epoch 46/300\n",
      "230/230 [==============================] - 0s 578us/step - loss: 0.4297 - acc: 0.8870\n",
      "Epoch 47/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.4048 - acc: 0.9043\n",
      "Epoch 48/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.4342 - acc: 0.9043\n",
      "Epoch 49/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.4192 - acc: 0.9087\n",
      "Epoch 50/300\n",
      "230/230 [==============================] - 0s 652us/step - loss: 0.4041 - acc: 0.9043\n",
      "Epoch 51/300\n",
      "230/230 [==============================] - 0s 539us/step - loss: 0.4338 - acc: 0.9043\n",
      "Epoch 52/300\n",
      "230/230 [==============================] - 0s 852us/step - loss: 0.4359 - acc: 0.8957\n",
      "Epoch 53/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.3721 - acc: 0.9304\n",
      "Epoch 54/300\n",
      "230/230 [==============================] - 0s 848us/step - loss: 0.4002 - acc: 0.9304\n",
      "Epoch 55/300\n",
      "230/230 [==============================] - 0s 743us/step - loss: 0.3886 - acc: 0.9043\n",
      "Epoch 56/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.3734 - acc: 0.9435\n",
      "Epoch 57/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.4117 - acc: 0.8957\n",
      "Epoch 58/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.3660 - acc: 0.9304\n",
      "Epoch 59/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.3777 - acc: 0.9174\n",
      "Epoch 60/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.3725 - acc: 0.9304\n",
      "Epoch 61/300\n",
      "230/230 [==============================] - 0s 543us/step - loss: 0.3691 - acc: 0.9217\n",
      "Epoch 62/300\n",
      "230/230 [==============================] - 0s 513us/step - loss: 0.4187 - acc: 0.8913\n",
      "Epoch 63/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.4214 - acc: 0.9000\n",
      "Epoch 64/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.3713 - acc: 0.9348\n",
      "Epoch 65/300\n",
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      "230/230 [==============================] - 0s 483us/step - loss: 0.2165 - acc: 0.9652\n",
      "Epoch 239/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2340 - acc: 0.9565\n",
      "Epoch 240/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2182 - acc: 0.9696\n",
      "Epoch 241/300\n",
      "230/230 [==============================] - 0s 448us/step - loss: 0.2281 - acc: 0.9609\n",
      "Epoch 242/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.1951 - acc: 0.9739\n",
      "Epoch 243/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2371 - acc: 0.9565\n",
      "Epoch 244/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2134 - acc: 0.9739\n",
      "Epoch 245/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2160 - acc: 0.9565\n",
      "Epoch 246/300\n",
      "230/230 [==============================] - 0s 413us/step - loss: 0.2332 - acc: 0.9565\n",
      "Epoch 247/300\n",
      "230/230 [==============================] - 0s 457us/step - loss: 0.2056 - acc: 0.9783\n",
      "Epoch 248/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1896 - acc: 0.9913\n",
      "Epoch 249/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1972 - acc: 0.9783\n",
      "Epoch 250/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2069 - acc: 0.9652\n",
      "Epoch 251/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2357 - acc: 0.9565\n",
      "Epoch 252/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.2199 - acc: 0.9652\n",
      "Epoch 253/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2129 - acc: 0.9783 0s - loss: 0.2218 - acc: 0.974\n",
      "Epoch 254/300\n",
      "230/230 [==============================] - 0s 430us/step - loss: 0.1790 - acc: 0.9957\n",
      "Epoch 255/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1751 - acc: 0.9913\n",
      "Epoch 256/300\n",
      "230/230 [==============================] - 0s 426us/step - loss: 0.2014 - acc: 0.9696\n",
      "Epoch 257/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.2299 - acc: 0.9609\n",
      "Epoch 258/300\n",
      "230/230 [==============================] - 0s 409us/step - loss: 0.2045 - acc: 0.9696\n",
      "Epoch 259/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2164 - acc: 0.9652\n",
      "Epoch 260/300\n",
      "230/230 [==============================] - 0s 439us/step - loss: 0.1991 - acc: 0.9696\n",
      "Epoch 261/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.2214 - acc: 0.9696\n",
      "Epoch 262/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2122 - acc: 0.9696\n",
      "Epoch 263/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2024 - acc: 0.9652\n",
      "Epoch 264/300\n",
      "230/230 [==============================] - 0s 404us/step - loss: 0.1998 - acc: 0.9696\n",
      "Epoch 265/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230/230 [==============================] - 0s 457us/step - loss: 0.1978 - acc: 0.9739\n",
      "Epoch 266/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1838 - acc: 0.9870\n",
      "Epoch 267/300\n",
      "230/230 [==============================] - 0s 422us/step - loss: 0.2595 - acc: 0.9565\n",
      "Epoch 268/300\n",
      "230/230 [==============================] - 0s 548us/step - loss: 0.1932 - acc: 0.9739\n",
      "Epoch 269/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2221 - acc: 0.9478\n",
      "Epoch 270/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.1786 - acc: 0.9826\n",
      "Epoch 271/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2222 - acc: 0.9522\n",
      "Epoch 272/300\n",
      "230/230 [==============================] - 0s 530us/step - loss: 0.2014 - acc: 0.9565\n",
      "Epoch 273/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2032 - acc: 0.9783\n",
      "Epoch 274/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2028 - acc: 0.9652\n",
      "Epoch 275/300\n",
      "230/230 [==============================] - 0s 496us/step - loss: 0.2170 - acc: 0.9739\n",
      "Epoch 276/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1990 - acc: 0.9739\n",
      "Epoch 277/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2018 - acc: 0.9696\n",
      "Epoch 278/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2117 - acc: 0.9739\n",
      "Epoch 279/300\n",
      "230/230 [==============================] - 0s 491us/step - loss: 0.2209 - acc: 0.9565\n",
      "Epoch 280/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.2026 - acc: 0.9783\n",
      "Epoch 281/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.2244 - acc: 0.9522\n",
      "Epoch 282/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2074 - acc: 0.9783\n",
      "Epoch 283/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.1920 - acc: 0.9870\n",
      "Epoch 284/300\n",
      "230/230 [==============================] - 0s 461us/step - loss: 0.2049 - acc: 0.9826\n",
      "Epoch 285/300\n",
      "230/230 [==============================] - 0s 483us/step - loss: 0.2064 - acc: 0.9652\n",
      "Epoch 286/300\n",
      "230/230 [==============================] - 0s 435us/step - loss: 0.1831 - acc: 0.9870\n",
      "Epoch 287/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1940 - acc: 0.9826\n",
      "Epoch 288/300\n",
      "230/230 [==============================] - 0s 465us/step - loss: 0.2070 - acc: 0.9696\n",
      "Epoch 289/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.1982 - acc: 0.9739\n",
      "Epoch 290/300\n",
      "230/230 [==============================] - 0s 470us/step - loss: 0.1929 - acc: 0.9739\n",
      "Epoch 291/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1830 - acc: 0.9739\n",
      "Epoch 292/300\n",
      "230/230 [==============================] - 0s 500us/step - loss: 0.1845 - acc: 0.9739\n",
      "Epoch 293/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.1793 - acc: 0.9783\n",
      "Epoch 294/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1882 - acc: 0.9826\n",
      "Epoch 295/300\n",
      "230/230 [==============================] - 0s 452us/step - loss: 0.2020 - acc: 0.9652\n",
      "Epoch 296/300\n",
      "230/230 [==============================] - 0s 474us/step - loss: 0.2138 - acc: 0.9739\n",
      "Epoch 297/300\n",
      "230/230 [==============================] - 0s 504us/step - loss: 0.1865 - acc: 0.9739\n",
      "Epoch 298/300\n",
      "230/230 [==============================] - 0s 487us/step - loss: 0.1781 - acc: 0.9826\n",
      "Epoch 299/300\n",
      "230/230 [==============================] - 0s 478us/step - loss: 0.1943 - acc: 0.9696\n",
      "Epoch 300/300\n",
      "230/230 [==============================] - 0s 443us/step - loss: 0.1957 - acc: 0.9652\n"
     ]
    }
   ],
   "source": [
    "for train_idx, test_idx in cv.split(labels):\n",
    "    \n",
    "    Csp = [];ss = [];nn = [] # empty lists\n",
    "    \n",
    "    label_train, label_test = labels[train_idx], labels[test_idx]\n",
    "    y_train, y_test = X_out[train_idx], X_out[test_idx]\n",
    "    \n",
    "    # CSP filter applied separately for all Frequency band coefficients\n",
    "    \n",
    "    Csp = [CSP(n_components=4, reg=None, log=True, norm_trace=False) for _ in range(8)]\n",
    "    ss = preprocessing.StandardScaler()\n",
    "\n",
    "    X_train = ss.fit_transform(np.concatenate(tuple(Csp[x].fit_transform(wpd_data[x,train_idx,:,:],label_train) for x  in range(8)),axis=-1))\n",
    "\n",
    "    X_test = ss.transform(np.concatenate(tuple(Csp[x].transform(wpd_data[x,test_idx,:,:]) for x  in range(8)),axis=-1))\n",
    "    \n",
    "    nn = build_classifier()  \n",
    "    \n",
    "    nn.fit(X_train, y_train, batch_size = 32, epochs = 300)\n",
    "    \n",
    "    y_pred = nn.predict(X_test)\n",
    "    pred = (y_pred == y_pred.max(axis=1)[:,None]).astype(int)\n",
    "\n",
    "    acc.append(accuracy_score(y_test.argmax(axis=1), pred.argmax(axis=1)))\n",
    "    ka.append(cohen_kappa_score(y_test.argmax(axis=1), pred.argmax(axis=1)))\n",
    "    prec.append(precision_score(y_test.argmax(axis=1), pred.argmax(axis=1),average='weighted'))\n",
    "    recall.append(recall_score(y_test.argmax(axis=1), pred.argmax(axis=1),average='weighted'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Accuracy     Kappa  Precision    Recall\n",
      "Fold                                         \n",
      "F1    0.672414  0.564255   0.691109  0.672414\n",
      "F2    0.689655  0.586862   0.705361  0.689655\n",
      "F3    0.706897  0.610585   0.735770  0.706897\n",
      "F4    0.637931  0.515320   0.670463  0.637931\n",
      "F5    0.758621  0.679811   0.785676  0.758621\n",
      "F6    0.620690  0.493852   0.659674  0.620690\n",
      "F7    0.672414  0.556003   0.686803  0.672414\n",
      "F8    0.741379  0.649476   0.755131  0.741379\n",
      "F9    0.637931  0.505280   0.635285  0.637931\n",
      "F10   0.810345  0.743981   0.825924  0.810345\n",
      "Avg   0.694828  0.590542   0.715120  0.694828\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "scores = {'Accuracy':acc,'Kappa':ka,'Precision':prec,'Recall':recall}\n",
    "\n",
    "Es = pd.DataFrame(scores)\n",
    "\n",
    "avg = {'Accuracy':[np.mean(acc)],'Kappa':[np.mean(ka)],'Precision':[np.mean(prec)],'Recall':[np.mean(recall)]}\n",
    "\n",
    "Avg = pd.DataFrame(avg)\n",
    "\n",
    "\n",
    "T = pd.concat([Es,Avg])\n",
    "\n",
    "T.index = ['F1','F2','F3','F4','F5','F6','F7','F8','F9','F10','Avg']\n",
    "T.index.rename('Fold',inplace=True)\n",
    "\n",
    "print(T)"
   ]
  }
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